youseon/seoul_test · Datasets at Hugging Face

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<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "pasta, milk, cheese, potato, Allium cepa, butter, mushroom, egg, sausage, smoked meat"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Europe, Indonesia"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"pasta, milk, cheese, potato, Allium cepa, butter, mushroom, egg, sausage, smoked meat"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Europe, Indonesia"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_9: nan table_piece_4: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12],"data":[["Baseline","n","Gene name","Sequence","Gene","Name"],["OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI"],["95 % CI","41",null,null,null,null],["Sample",null,null,null,null,null],["References","33",null,null,null,null],["t",null,null,null,null,"48"],["Control",null,null,null,"14","44"],["Sequence",null,"35",null,null,null],["Gene name",null,null,null,"72",null],["Case",null,"13","88",null,null],["Category","89",null,null,null,null],["SE",null,null,"87",null,"7"],["HR",null,"43",null,"33","21"]]} table_piece_5: {"columns":["0","1","2","3","4","5"],"index":[13,14,15,16,17,18,19,20,21,22],"data":[["Gene",null,null,null,"39","50"],["t",null,"36",null,null,null],["N %",null,"64",null,"10",null],["N %",null,null,null,null,null],["Location",null,null,null,null,null],["Genotype",null,null,null,"29",null],["Sample",null,null,null,null,null],["Variable",null,null,"7",null,null],["p Value",null,null,null,"52",null],["p - Value",null,null,null,null,null]]} table_piece_3: nan table_piece_7: {"columns":["0","1","2","3"],"index":[0,1,2,3,4,5,6,7],"data":[["Country","Treatment","Treatment","Controls"],[null,null,null,null],["Female",null,null,null],["Source","25",null,null],["P",null,"0",null],[null,null,"55",null],["A",null,null,null],[null,null,"38",null]]} table_piece_1: {"columns":["0","1","2","3","4","5","6","7","8"],"index":[0,1,2,3],"data":[["Specificity","p","No.","Case","Region","Reference","Case","Case","Case"],["Characteristic",null,null,null,null,null,null,null,"54"],["Species",null,"24","2",null,null,null,null,null],[null,null,null,null,null,null,null,"25",null]]} table_piece_8: {"columns":["4","5","6","7"],"index":[0,1,2,3,4,5,6,7],"data":[["F","M","p - value","SD"],[null,null,"16",null],[null,null,null,null],[null,null,null,null],[null,null,null,"95"],[null,"49",null,null],[null,"64",null,null],[null,"72",null,null]]} table_piece_6: nan table_piece_2: {"columns":["0","1","2","3","4","5","6","7","8"],"index":[4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"data":[["p Value","25","48",null,null,null,null,null,null],[null,"0",null,null,null,null,"79",null,null],["Total",null,null,null,null,null,null,null,null],["M",null,null,null,null,"33",null,null,null],[null,null,null,null,null,null,null,null,null],["Name",null,null,null,null,null,null,null,null],["Mean \u00b1 SD",null,null,"37",null,null,null,null,null],["Name","99",null,null,null,"60",null,null,null],["Patient",null,"42","1",null,"47",null,null,null],["Patient",null,null,null,null,null,"92",null,null],["Median",null,"15",null,"60","71",null,"33",null],["All",null,null,null,null,"91",null,null,null],["Name",null,"90","36",null,"53",null,"99",null],[null,null,"98",null,null,null,null,null,null],["Female",null,"41",null,null,null,null,null,null],["Patient","14",null,null,"98",null,null,null,null],["Gene name",null,null,null,null,"71",null,null,null],["N %",null,null,"41",null,null,null,"25",null],["Sex",null,"54",null,null,null,"75",null,null],["P value",null,null,"60","70",null,null,null,null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "data": [ [ "Specificity", "p", "No.", "Case", "Region", "Reference", "Case", "Case", "Case" ], [ "Characteristic", null, null, null, null, null, null, null, "54" ], [ "Species", null, "24", "2", null, null, null, null, null ], [ null, null, null, null, null, null, null, "25", null ], [ "p Value", "25", "48", null, null, null, null, null, null ], [ null, "0", null, null, null, null, "79", null, null ], [ "Total", null, null, null, null, null, null, null, null ], [ "M", null, null, null, null, "33", null, null, null ], [ null, null, null, null, null, null, null, null, null ], [ "Name", null, null, null, null, null, null, null, null ], [ "Mean ± SD", null, null, "37", null, null, null, null, null ], [ "Name", "99", null, null, null, "60", null, null, null ], [ "Patient", null, "42", "1", null, "47", null, null, null ], [ "Patient", null, null, null, null, null, "92", null, null ], [ "Median", null, "15", null, "60", "71", null, "33", null ], [ "All", null, null, null, null, "91", null, null, null ], [ "Name", null, "90", "36", null, "53", null, "99", null ], [ null, null, "98", null, null, null, null, null, null ], [ "Female", null, "41", null, null, null, null, null, null ], [ "Patient", "14", null, null, "98", null, null, null, null ], [ "Gene name", null, null, null, null, "71", null, null, null ], [ "N %", null, null, "41", null, null, null, "25", null ], [ "Sex", null, "54", null, null, null, "75", null, null ], [ "P value", null, null, "60", "70", null, null, null, null ] ] }, "table_2": { "columns": [ "0", "1", "2", "3", "4", "5" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 ], "data": [ [ "Baseline", "n", "Gene name", "Sequence", "Gene", "Name" ], [ "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI" ], [ "95 % CI", "41", null, null, null, null ], [ "Sample", null, null, null, null, null ], [ "References", "33", null, null, null, null ], [ "t", null, null, null, null, "48" ], [ "Control", null, null, null, "14", "44" ], [ "Sequence", null, "35", null, null, null ], [ "Gene name", null, null, null, "72", null ], [ "Case", null, "13", "88", null, null ], [ "Category", "89", null, null, null, null ], [ "SE", null, null, "87", null, "7" ], [ "HR", null, "43", null, "33", "21" ], [ "Gene", null, null, null, "39", "50" ], [ "t", null, "36", null, null, null ], [ "N %", null, "64", null, "10", null ], [ "N %", null, null, null, null, null ], [ "Location", null, null, null, null, null ], [ "Genotype", null, null, null, "29", null ], [ "Sample", null, null, null, null, null ], [ "Variable", null, null, "7", null, null ], [ "p Value", null, null, null, "52", null ], [ "p - Value", null, null, null, null, null ] ] }, "table_3": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "data": [ [ "Country", "Treatment", "Treatment", "Controls", "F", "M", "p - value", "SD" ], [ null, null, null, null, null, null, "16", null ], [ "Female", null, null, null, null, null, null, null ], [ "Source", "25", null, null, null, null, null, null ], [ "P", null, "0", null, null, null, null, "95" ], [ null, null, "55", null, null, "49", null, null ], [ "A", null, null, null, null, "64", null, null ], [ null, null, "38", null, null, "72", null, null ] ] } }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Mulo diye msur dal"}, {"Prop0": "Prop3", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Mulo diye musur dal"},{"Prop0":"Prop3","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "0RFA()", "col1": "Category:Kiwifruit", "col2": "()q44S, ()q\|4&, ()q\|4&, ()q\|4&, ()q\|4&, ()q\|4&, ()q\|4&, ()q\|4&, ()q\|4&"}, {"col0": "OKEA1", "col1": "Category:Nephelium lappaceum", "col2": "()q3()\|"}, {"col0": "OKEA2", "col1": "Gategory:AVocado", "col2": "09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038"}, {"col0": "OKEA3", "col1": "Gategory:Mangoes", "col2": "()q\|7G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G"}, {"col0": "OKEA4", "col1": "Gategory:0ranges (fruit)", "col2": "09202, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216"}, {"col0": "OKEA5", "col1": "Gategory:Crapefruit", "col2": "()q\|\|&, ()q\|\|Z, ()q\|\|7, ()q\|\|3, ()q\|\|4, ()q\|Z(), ()q\|\|q, ()q\|\|\|, ()q\|\|G, ()q\|Z\|, ()q\|\|Z, ()q\|\|7, ()q\|\|3, ()q\|\|4, ()q\|Z(), ()q\|\|q, ()q\|\|\|, ()q\|\|G, ()q\|Z\|, ()q\|\|Z, ()q\|\|7, ()q\|\|3, ()q\|\|4, ()q\|Z(), ()q\|\|q, ()q\|\|\|, ()q\|\|G, ()q\|Z\|, ()q\|\|Z, ()q\|\|7, ()q\|\|3, ()q\|\|4, ()q\|Z(), ()q\|\|q, ()q\|\|\|, ()q\|\|G, ()q\|Z\|, ()q\|\|Z, ()q\|\|7, ()q\|\|3, ()q\|\|4, ()q\|Z(), ()q\|\|q, ()q\|\|\|, ()q\|\|G, ()q\|Z\|"}, {"col0": "OKEA6", "col1": "Gategory:Pineapp1es", "col2": "()q4Zq, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7\|, ()qZ7Z, ()q3S4"}, {"col0": "OKEA7", "col1": "Category:Cherries", "col2": "09070, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076"}, {"col0": "0RFA&", "col1": "Gategory:01iVes", "col2": "09194, 09193, 09195, 09193, 09195"}, {"col0": "0RFAq", "col1": "Gategory:8ananas", "col2": "09040, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041"}, {"col0": "0RFA\|()", "col1": "Category:Limes (fruit)", "col2": "09159, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160"}, {"col0": "OKEA11", "col1": "Category:Apricots", "col2": "()q()Z4, ()q()3\|, ()q()3S, 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"09084"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"OKEA0","col1":"Category:Kiwifruit","col2":"09445, 09148, 09148, 09148, 09148, 09148, 09148, 09148, 09148"},{"col0":"OKEA1","col1":"Category:Nephelium lappaceum","col2":"09301"},{"col0":"OKEA2","col1":"Category:Avocado","col2":"09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038"},{"col0":"OKEA3","col1":"Category:Mangoes","col2":"09176, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436"},{"col0":"OKEA4","col1":"Category:Oranges (fruit)","col2":"09202, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216"},{"col0":"OKEA5","col1":"Category:Grapefruit","col2":"09118, 09112, 09117, 09113, 09114, 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<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Ben"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5","6","7","8","9","10","0","1","2","3","0","1","2","3","4","5","6","7","8","9"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","N \/ A","California","Cash provided by operating activities","Noninterest income","Total loans","8 - K","8 - K","Properties","Deferred","TX","Current portion of long - term debt","a","Less : Net income attributable to noncontrolling interests","Receivables","Receivables","Receivables","Receivables","Receivables","Receivables"],[null,"Deferred tax liabilities :","Total liabilities and shareholders \u2019 equity","Other Benefits","Retained Earnings","F - 5","Other income expense","Current","Current","Current","% of Total","Distribution Center","0.11 %","76.58 %","51.71 %","A3","Current portion of long - term debt","Entergy Louisiana","Period","Other expense, net","Accounts payable","Average interest rate","Average interest rate","Average interest rate","Comprehensive income"],["Total consumer","98582.65 $","7657.56 $","15622.65 $",null,"62660.86 $",null,"66362.2 $","26381.11 $","59640.56 $","59640.56 $",null,"34.91 %","32.95 %","67.31 %",null,"Current portion of long - term debt",null,"Eliminations","Eliminations","Eliminations","Average interest rate","Average interest rate","Average interest rate","Construction in progress"],["in millions","52361.25 $","76119.68 $","15622.65 $","41527.17 $","89592.61 $","87726.74 $","16529.89 $","72576.23 $","52887.91 $","32729.75 $","Thereafter","65.16 %","27.88 %","41.10 %","Industrial","48.71 %","98.61 %","37.89 %",null,"40.67 %","47.86 %",null,"68.96 %","17.59 %"],["Balance, beginning of period","89992.16 $","98263.49 $","45856.35 $","39151.56 $","99174.62 $","21302.86 $","88692.60 $","49973.62 $","5553.74 $",null,"Income from continuing operations before income taxes","64.2 %","66.48 %","66.48 %","Interest paid","85.93 %","97.83 %","97.83 %","48.5 %","40.67 %","86.74 %","63.3 %","65.62 %","30.32 %"],["Total costs and expenses","20028.66 $","64455.70 $","80157.53 $",null,"72220.70 $","68872.28 $","61785.11 $","99079.45 $","38007.18 $","99840.38 $","Product","Product","66.48 %","66.48 %","Preferred stock dividends","84.70 %","6.69 %","90.25 %","53.80 %","38.22 %","99.4 %","53.82 %","25.48 %","18.15 %"],["Euro","24067.59 $","75731.69 $","80157.53 $",null,"20474.39 $","16743.64 $","82057.80 $","5530.5 $","48327.31 $","56947.64 $","Baa1","17.98 %","20.19 %","7.40 %","Expenses","18.33 %","18.33 %","18.33 %","80.55 %","32.42 %","94.46 %","47.69 %","53.47 %","18.15 %"],["ASSETS","20008.20 $","20008.20 $","20008.20 $",null,"74560.12 $","98464.87 $","66792.45 $",null,"37456.23 $","78204.56 $","Various","86.18 %",null,"7.40 %",null,"94.90 %","94.38 %",null,"80.55 %","0.34 %","0.34 %","0.34 %","0.34 %","0.34 %"],["Texas","16566.25 $","90074.81 $","29735.61 $",null,"19827.38 $",null,"34398.39 $",null,null,"69137.78 $","Location","46.31 %","46.31 %","7.40 %","June 30","17.53 %","77.69 %","89.85 %","66.57 %","16.88 %","49.40 %","12.14 %","14.1 %","85.94 %"],["Current assets :","62575.58 $","76594.23 $",null,null,"56486.2 $","56486.2 $","56486.2 $","56486.2 $","16135.26 $","11895.98 $","S - 1","46.31 %","46.31 %","85.85 %",null,"49.34 %","58.89 %","0.56 %","47.66 %","87.48 %","51.51 %","76.55 %","72.84 %","43.40 %"],["Other assets","Other assets","Other assets",null,null,"1898.4 $","99139.41 $","13978.85 $","22032.82 $","16135.26 $","52566.15 $","Benefits paid","99.85 %","94.49 %","50.12 %","Prior service credit","16.24 %","7.91 %","52.9 %",null,"6.21 %","97.69 %","48.81 %","68.75 %","98.99 %"],["Total loans and leases","33898.68 $","68041.32 $","75870.36 $",null,"61080.19 $","21091.19 $","14934.49 $","46804.58 $","97792.4 $","97792.4 $",null,null,null,null,null,null,null,null,null,null,null,null,null,null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{'table_1': '{"columns":["0","1","2","3","4","5","6","7","8","9","10"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","N \\/ A","California","Cash provided by operating activities","Noninterest income","Total loans"],[null,"Deferred tax liabilities :","Total liabilities and shareholders \\u2019 equity","Other Benefits","Retained Earnings","F - 5","Other income expense","Current","Current","Current","% of Total"],["Total consumer","98582.65 $","7657.56 $","15622.65 $",null,"62660.86 $",null,"66362.2 $","26381.11 $","59640.56 $","59640.56 $"],["in millions","52361.25 $","76119.68 $","15622.65 $","41527.17 $","89592.61 $","87726.74 $","16529.89 $","72576.23 $","52887.91 $","32729.75 $"],["Balance, beginning of period","89992.16 $","98263.49 $","45856.35 $","39151.56 $","99174.62 $","21302.86 $","88692.60 $","49973.62 $","5553.74 $",null],["Total costs and expenses","20028.66 $","64455.70 $","80157.53 $",null,"72220.70 $","68872.28 $","61785.11 $","99079.45 $","38007.18 $","99840.38 $"],["Euro","24067.59 $","75731.69 $","80157.53 $",null,"20474.39 $","16743.64 $","82057.80 $","5530.5 $","48327.31 $","56947.64 $"],["ASSETS","20008.20 $","20008.20 $","20008.20 $",null,"74560.12 $","98464.87 $","66792.45 $",null,"37456.23 $","78204.56 $"],["Texas","16566.25 $","90074.81 $","29735.61 $",null,"19827.38 $",null,"34398.39 $",null,null,"69137.78 $"],["Current assets :","62575.58 $","76594.23 $",null,null,"56486.2 $","56486.2 $","56486.2 $","56486.2 $","16135.26 $","11895.98 $"],["Other assets","Other assets","Other assets",null,null,"1898.4 $","99139.41 $","13978.85 $","22032.82 $","16135.26 $","52566.15 $"],["Total loans and leases","33898.68 $","68041.32 $","75870.36 $",null,"61080.19 $","21091.19 $","14934.49 $","46804.58 $","97792.4 $","97792.4 $"]]}', 'table_2': '{"columns":["0","1","2","3"],"index":[0,1,2,3,4,5,6,7,8,9,10],"data":[["8 - K","8 - K","Properties","Deferred"],["Distribution Center","0.11 %","76.58 %","51.71 %"],[null,"34.91 %","32.95 %","67.31 %"],["Thereafter","65.16 %","27.88 %","41.10 %"],["Income from continuing operations before income taxes","64.2 %","66.48 %","66.48 %"],["Product","Product","66.48 %","66.48 %"],["Baa1","17.98 %","20.19 %","7.40 %"],["Various","86.18 %",null,"7.40 %"],["Location","46.31 %","46.31 %","7.40 %"],["S - 1","46.31 %","46.31 %","85.85 %"],["Benefits paid","99.85 %","94.49 %","50.12 %"]]}', 'table_3': '{"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[0,1,2,3,4,5,6,7,8,9,10],"data":[["TX","Current portion of long - term debt","a","Less : Net income attributable to noncontrolling interests","Receivables","Receivables","Receivables","Receivables","Receivables","Receivables"],["A3","Current portion of long - term debt","Entergy Louisiana","Period","Other expense, net","Accounts payable","Average interest rate","Average interest rate","Average interest rate","Comprehensive income"],[null,"Current portion of long - term debt",null,"Eliminations","Eliminations","Eliminations","Average interest rate","Average interest rate","Average interest rate","Construction in progress"],["Industrial","48.71 %","98.61 %","37.89 %",null,"40.67 %","47.86 %",null,"68.96 %","17.59 %"],["Interest paid","85.93 %","97.83 %","97.83 %","48.5 %","40.67 %","86.74 %","63.3 %","65.62 %","30.32 %"],["Preferred stock dividends","84.70 %","6.69 %","90.25 %","53.80 %","38.22 %","99.4 %","53.82 %","25.48 %","18.15 %"],["Expenses","18.33 %","18.33 %","18.33 %","80.55 %","32.42 %","94.46 %","47.69 %","53.47 %","18.15 %"],[null,"94.90 %","94.38 %",null,"80.55 %","0.34 %","0.34 %","0.34 %","0.34 %","0.34 %"],["June 30","17.53 %","77.69 %","89.85 %","66.57 %","16.88 %","49.40 %","12.14 %","14.1 %","85.94 %"],[null,"49.34 %","58.89 %","0.56 %","47.66 %","87.48 %","51.51 %","76.55 %","72.84 %","43.40 %"],["Prior service credit","16.24 %","7.91 %","52.9 %",null,"6.21 %","97.69 %","48.81 %","68.75 %","98.99 %"]]}'}<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "o\|el", "col1": "Velko", "col2": 7.53}, {"col0": "oh Wa", "col1": "Hohe", "col2": 4.7094}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"o\|el","col1":"Velkopopovický Kozel","col2":7.53},{"col0":"oh Wand","col1":"Hohe Wand","col2":4.7094}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "S", "col1": 1.0}, {"col0": "Sam", "col1": 1.0}, {"col0": "Sams", "col1": 2.0}, {"col0": "Red", "col1": 2.0}, {"col0": "Sams", "col1": 2.0}, {"col0": "Sam", "col1": 1.5}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"Samsung Galaxy J1 Ace","col1":1.0},{"col0":"Samsung Galaxy J1 mini prime","col1":1.0},{"col0":"Samsung Galaxy A10","col1":2.0},{"col0":"Redmi 7A","col1":2.0},{"col0":"Samsung Galaxy J5 Prime","col1":2.0},{"col0":"Samsung Galaxy J2 (2016)","col1":1.5}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_2: {"columns":["2"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Primer"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse"],["Extension :"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse :"],["Extension :"]]} table_piece_4: nan table_piece_7: nan table_piece_8: nan table_piece_6: nan table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Table 2. Primer sequences used in genotyping for the Pit - 1 gene","SNP"],["Sequence ( 5 \u2032 to 3 \u2032)","rs13687126,"],[null,"ACGTTGGATGTTGAATGTGAGAGGAGAAGG"],[null,"ACGTTGGATGTCCTCTGTCAATGCCATCTC"],["AGCCCCACAATGACA","rs13687128"],[null,"ACGTTGGATGCTCAAGATTAAGCCCCTCAG"],[null,"ACGTTGGATGGGCACTTTGGAGAACAAAGC"],["AACGCCTTCTTCTCAGGAAAT","rs13905624"],[null,"ACGTTGGATGTTTATACGTAGGCACAACAG"],[null,"ACGTTGGATGTAAAGTGCCCTCTTGTGTCC"],["GCTTAAGCCAGCTTTGG,","rs13905622,"],[null,"ACGTTGGATGCAGCTGCTGAGGAAATCAAG"],[null,"ACGTTGGATGTAGCCTCAGTGTAATGAGGG"],["TGTAATGAGGGTAGATATAAAAG","rs13905628,"],[null,"ACGTTGGATGGTCTGAGAGCTACAATCGTG"],[null,"ACGTTGGATGGAGGGGAATGGTTTTTGTGG"]]} table_piece_9: nan table_piece_5: nan table_piece_3: nan<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ], "data": [ [ "Table 2. Primer sequences used in genotyping for the Pit - 1 gene", "SNP", "Primer" ], [ "Sequence ( 5 ′ to 3 ′)", "rs13687126,", "Forward" ], [ null, "ACGTTGGATGTTGAATGTGAGAGGAGAAGG", "Reverse :" ], [ null, "ACGTTGGATGTCCTCTGTCAATGCCATCTC", "Extension :" ], [ "AGCCCCACAATGACA", "rs13687128", "Forward" ], [ null, "ACGTTGGATGCTCAAGATTAAGCCCCTCAG", "Reverse" ], [ null, "ACGTTGGATGGGCACTTTGGAGAACAAAGC", "Extension :" ], [ "AACGCCTTCTTCTCAGGAAAT", "rs13905624", "Forward" ], [ null, "ACGTTGGATGTTTATACGTAGGCACAACAG", "Reverse :" ], [ null, "ACGTTGGATGTAAAGTGCCCTCTTGTGTCC", "Extension :" ], [ "GCTTAAGCCAGCTTTGG,", "rs13905622,", "Forward" ], [ null, "ACGTTGGATGCAGCTGCTGAGGAAATCAAG", "Reverse :" ], [ null, "ACGTTGGATGTAGCCTCAGTGTAATGAGGG", "Extension :" ], [ "TGTAATGAGGGTAGATATAAAAG", "rs13905628,", "Forward" ], [ null, "ACGTTGGATGGTCTGAGAGCTACAATCGTG", "Reverse :" ], [ null, "ACGTTGGATGGAGGGGAATGGTTTTTGTGG", "Extension :" ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "Le8ron James", "col1": "St. Vincent\u2013St. Mary High School", "col2": "Dom James"}, {"col0": "A1i", "col1": "Central City High School", "col2": "Lita"}, {"col0": "Tony 5tark", "col1": "Massachusetts Institute of Technology", "col2": "Morgan Stark"}, {"col0": "Xue Pan", "col1": "Jia's Family School", "col2": "son of Xiangling"}, {"col0": "Phi1ip 8anks", "col1": "Princeton University", "col2": "Ashley Banks"}, {"col0": "Yukiko Kudo", "col1": "Teitan Elementary School", "col2": "Gonan FdogaWa"}, {"col0": "Josiah Bartlet", "col1": "Dartmouth Go11ege", "col2": "Eleanor Bartlet"}, {"col0": "Wa1ter 8ishop", "col1": "Harvard University", "col2": "Peter Bishop"}, {"col0": "Batman", "col1": "University of Cambridge", "col2": "Cassandra Cain"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"LeBron James","col1":"St. Vincent–St. Mary High School","col2":"Dom James"},{"col0":"Ali","col1":"Central City High School","col2":"Lita"},{"col0":"Tony Stark","col1":"Massachusetts Institute of Technology","col2":"Morgan Stark"},{"col0":"Xue Pan","col1":"Jia's Family School","col2":"son of Xiangling"},{"col0":"Philip Banks","col1":"Princeton University","col2":"Ashley Banks"},{"col0":"Yukiko Kudo","col1":"Teitan Elementary School","col2":"Conan Edogawa"},{"col0":"Josiah Bartlet","col1":"Dartmouth College","col2":"Eleanor Bartlet"},{"col0":"Walter Bishop","col1":"Harvard University","col2":"Peter Bishop"},{"col0":"Batman","col1":"University of Cambridge","col2":"Cassandra Cain"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_6: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4,5,6,7,8],"data":[["95 % CI",null,"SE","Name","Method"],["Male","Value","Range","\u03b2","Method"],["Mean SD","Female","Source","Control","Method"],["B",null,null,null,null],["All",null,null,null,null],[null,"1",null,"49","90"],["Genotype","51",null,null,null],["Sensitivity",null,null,null,null],["Study",null,null,null,null]]} table_piece_7: nan table_piece_9: nan table_piece_4: nan table_piece_8: nan table_piece_3: nan table_piece_2: {"columns":["0","1","2","3","4"],"index":[9,10,11,12],"data":[["B","81",null,null,null],["Number",null,"75","26",null],["Location",null,"70","34",null],[null,null,"93",null,null]]} table_piece_5: nan<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ], "data": [ [ "95 % CI", null, "SE", "Name", "Method" ], [ "Male", "Value", "Range", "β", "Method" ], [ "Mean SD", "Female", "Source", "Control", "Method" ], [ "B", null, null, null, null ], [ "All", null, null, null, null ], [ null, "1", null, "49", "90" ], [ "Genotype", "51", null, null, null ], [ "Sensitivity", null, null, null, null ], [ "Study", null, null, null, null ], [ "B", "81", null, null, null ], [ "Number", null, "75", "26", null ], [ "Location", null, "70", "34", null ], [ null, null, "93", null, null ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_3: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4],"data":[["Cases",null,null,null,null],["Mean","Median","References","C","Baseline"],["Sensitivity",null,"31","92",null],["Frequency",null,null,null,null],["p - value",null,null,null,null]]} table_piece_2: {"columns":["0","1","2","3","4"],"index":[5,6,7,8,9,10,11,12,13,14],"data":[["Location",null,null,null,null],["Percentage","88",null,"75",null],["No",null,null,"53","45"],["Gene",null,null,null,"1"],["Category",null,null,null,null],["Location",null,null,null,null],[null,null,null,null,null],["Baseline",null,null,null,null],["Male",null,null,null,"51"],["Source",null,null,null,null]]} table_piece_7: nan table_piece_5: nan table_piece_9: nan table_piece_8: nan table_piece_4: nan table_piece_6: nan<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ], "data": [ [ "Cases", null, null, null, null ], [ "Mean", "Median", "References", "C", "Baseline" ], [ "Sensitivity", null, "31", "92", null ], [ "Frequency", null, null, null, null ], [ "p - value", null, null, null, null ], [ "Location", null, null, null, null ], [ "Percentage", "88", null, "75", null ], [ "No", null, null, "53", "45" ], [ "Gene", null, null, null, "1" ], [ "Category", null, null, null, null ], [ "Location", null, null, null, null ], [ null, null, null, null, null ], [ "Baseline", null, null, null, null ], [ "Male", null, null, null, "51" ], [ "Source", null, null, null, null ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop\|", "specific food preparation with cooking finished, and ready to eat, or be served": "Poland"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "uszka"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Belarusian cuisine"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "Russian cuisine, Polish cuisine, Ukrainian cuisine, Lithuanian cuisine"}, {"Prop0": "PropS", "specific food preparation with cooking finished, and ready to eat, or be served": "Meyers Konversations-Lexikon, 4th edition (1885\u20131890)"}, {"Prop0": "PropG", "specific food preparation with cooking finished, and ready to eat, or be served": "dough, edib1e mushroom, minced meat"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "Uszka"}, {"Prop0": "Prop8", "specific food preparation with cooking finished, and ready to eat, or be served": "Uszka"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Poland"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"uszka"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Belarusian cuisine"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"Russian cuisine, Polish cuisine, Ukrainian cuisine, Lithuanian cuisine"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Meyers Konversations-Lexikon, 4th edition (1885–1890)"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"dough, edible mushroom, minced meat"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"Uszka"},{"Prop0":"Prop8","specific food preparation with cooking finished, and ready to eat, or be served":"Uszka"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,0,1,2,3,4,5,6,7,8,9,10,11],"data":[[null,"Specificity","Specificity",null,null,null],[null,null,null,null,null,null],["Gender",null,null,null,null,null],["Characteristic",null,null,null,null,null],["Frequency",null,null,null,null,null],[null,"97",null,null,null,null],["Baseline",null,null,null,null,null],["Case",null,null,null,null,null],["Controls",null,null,null,null,null],["\u03b2","SE","N %",null,"Gender",null],["Cases",null,"Sequence","Sequence","Frequency","df"],["Mean","P","Sample","Sample","Species","Characteristic"],["Region","66","86",null,null,null],["Strain",null,null,"12",null,null],["Gene",null,null,null,null,"79"],["\u03b2","24",null,null,null,"30"],["Range",null,"90","55",null,null],["B",null,null,null,"78",null],["N",null,null,null,null,null],["Variable",null,null,null,null,null],["Location","23",null,null,null,null],["Cases",null,null,null,null,null],[null,null,null,"83",null,null],["n","63",null,null,"80",null],["Location",null,"44",null,null,null],[null,"80","93",null,null,null],["Frequency",null,"61",null,null,null],["Species",null,null,null,"82","31"],["p value",null,null,null,null,null],["Male",null,null,null,"27","78"],["Description",null,"96",null,null,null],["Range",null,"56",null,null,null],[null,null,null,null,"91",null],["n %","n %","Description",null,null,null],["Range","Percentage","Baseline",null,null,null],["Group","Name","Type",null,null,null],["Item","95",null,null,null,null],["Women",null,null,null,null,null],["\u03b2",null,null,null,null,null],["Group",null,null,null,null,null],["F","42",null,null,null,null],["N %",null,null,null,null,null],["df",null,null,null,null,null],["N",null,null,null,null,null],["P - value","59",null,null,null,null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{'table_1': '{"columns":["0","1","2"],"index":[0,1,2,3,4,5,6,7,8],"data":[[null,"Specificity","Specificity"],[null,null,null],["Gender",null,null],["Characteristic",null,null],["Frequency",null,null],[null,"97",null],["Baseline",null,null],["Case",null,null],["Controls",null,null]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"data":[["\\u03b2","SE","N %",null,"Gender",null],["Cases",null,"Sequence","Sequence","Frequency","df"],["Mean","P","Sample","Sample","Species","Characteristic"],["Region","66","86",null,null,null],["Strain",null,null,"12",null,null],["Gene",null,null,null,null,"79"],["\\u03b2","24",null,null,null,"30"],["Range",null,"90","55",null,null],["B",null,null,null,"78",null],["N",null,null,null,null,null],["Variable",null,null,null,null,null],["Location","23",null,null,null,null],["Cases",null,null,null,null,null],[null,null,null,"83",null,null],["n","63",null,null,"80",null],["Location",null,"44",null,null,null],[null,"80","93",null,null,null],["Frequency",null,"61",null,null,null],["Species",null,null,null,"82","31"],["p value",null,null,null,null,null],["Male",null,null,null,"27","78"],["Description",null,"96",null,null,null],["Range",null,"56",null,null,null],[null,null,null,null,"91",null]]}', 'table_3': '{"columns":["0","1","2"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["n %","n %","Description"],["Range","Percentage","Baseline"],["Group","Name","Type"],["Item","95",null],["Women",null,null],["\\u03b2",null,null],["Group",null,null],["F","42",null],["N %",null,null],["df",null,null],["N",null,null],["P - value","59",null]]}'}<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "grits"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Wiki Loves Cultura Popular Brasil - Culin\u00e1ria"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": null}, {"Prop0": null, "specific food preparation with cooking finished, and ready to eat, or be served": null}, {"Prop0": null, "specific food preparation with cooking finished, and ready to eat, or be served": "Italian cuisine"}, {"Prop0": "Prop6", "specific food preparation with cooking finished, and ready to eat, or be served": "Polenta"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "m\u0103m\u0103lig\u0103, Ka\u010damak"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"grits"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Wiki Loves Cultura Popular Brasil - Culinária"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Italy"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"cornmeal"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Italian cuisine"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"Polenta"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"mămăligă, Kačamak"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_3: {"columns":["6","7","8"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Age","Genotype","OR 95 % CI"],["Treatment","Outcome","93.21 %"],["P - value","P - value","Variable"],[null,null,null],[null,null,null],[null,null,null],[null,null,null],[null,null,null],[null,"47",null],[null,null,"2"],["73",null,"53"],["45",null,null],[null,null,"89"],[null,null,null],[null,null,"29"],[null,null,null]]} table_piece_4: nan table_piece_5: nan table_piece_2: {"columns":["2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["OR 95 % CI",null,"Gene","Mean \u00b1 SD"],["Gene","Gene",null,"P"],["Gene","Gene","t","P - value"],["55",null,null,null],[null,"0",null,"40"],[null,"27",null,null],["63",null,null,null],["95",null,null,null],[null,null,null,null],[null,null,null,"96"],[null,null,null,"33"],[null,null,null,null],["22",null,null,"53"],[null,null,null,null],[null,null,null,null],[null,null,null,null]]} table_piece_8: nan table_piece_6: nan table_piece_9: nan table_piece_7: nan table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Country","Total"],["p - Value","OR 95 % CI"],["Factor","Strain"],["p - value","18"],["Characteristics",null],[null,null],["Factor",null],["n %",null],["Specificity",null],["Female",null],["B","5"],["Age years",null],["SD","89"],["Variable",null],["Univariate analysis",null],["Species",null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ], "data": [ [ "Country", "Total", "OR 95 % CI", null, "Gene", "Mean ± SD", "Age", "Genotype", "OR 95 % CI" ], [ "p - Value", "OR 95 % CI", "Gene", "Gene", null, "P", "Treatment", "Outcome", "93.21 %" ], [ "Factor", "Strain", "Gene", "Gene", "t", "P - value", "P - value", "P - value", "Variable" ], [ "p - value", "18", "55", null, null, null, null, null, null ], [ "Characteristics", null, null, "0", null, "40", null, null, null ], [ null, null, null, "27", null, null, null, null, null ], [ "Factor", null, "63", null, null, null, null, null, null ], [ "n %", null, "95", null, null, null, null, null, null ], [ "Specificity", null, null, null, null, null, null, "47", null ], [ "Female", null, null, null, null, "96", null, null, "2" ], [ "B", "5", null, null, null, "33", "73", null, "53" ], [ "Age years", null, null, null, null, null, "45", null, null ], [ "SD", "89", "22", null, null, "53", null, null, "89" ], [ "Variable", null, null, null, null, null, null, null, null ], [ "Univariate analysis", null, null, null, null, null, null, null, "29" ], [ "Species", null, null, null, null, null, null, null, null ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "HKNS0", "col1": "Mexico", "col2": "Category:Atole"}, {"col0": "HKNS1", "col1": "Mexico", "col2": "Category:Aguas frescas"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"HKNS0","col1":"Mexico","col2":"Category:Atole"},{"col0":"HKNS1","col1":"Mexico","col2":"Category:Aguas frescas"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_6: nan table_piece_9: nan table_piece_7: nan table_piece_3: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[2,3,4,5,6],"data":[["Gender","SD","F","Univariate analysis","Gene name","P","P","P","Number","B","864.25","Age"],["Variables","97",null,"17",null,"85",null,null,null,null,null,null],["Strain",null,null,null,null,null,null,null,"61",null,null,null],["Gene","46",null,null,null,"95",null,"17","70",null,null,null],["N %",null,null,"16",null,null,null,null,null,null,null,null]]} table_piece_8: nan table_piece_1: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[0],"data":[["Women","Variables","F","Percentage","Percentage","p Value","OR","References",null,"Controls","Variable","Gene"]]} table_piece_5: nan table_piece_4: nan table_piece_2: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[1],"data":[["Genotype","SD","F","Percentage","Percentage","Women","Men","Male","Variables",null,"Characteristic","Item"]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11" ], "index": [ 0, 1, 2, 3, 4, 5, 6 ], "data": [ [ "Women", "Variables", "F", "Percentage", "Percentage", "p Value", "OR", "References", null, "Controls", "Variable", "Gene" ], [ "Genotype", "SD", "F", "Percentage", "Percentage", "Women", "Men", "Male", "Variables", null, "Characteristic", "Item" ], [ "Gender", "SD", "F", "Univariate analysis", "Gene name", "P", "P", "P", "Number", "B", "864.25", "Age" ], [ "Variables", "97", null, "17", null, "85", null, null, null, null, null, null ], [ "Strain", null, null, null, null, null, null, null, "61", null, null, null ], [ "Gene", "46", null, null, null, "95", null, "17", "70", null, null, null ], [ "N %", null, null, "16", null, null, null, null, null, null, null, null ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>Here is combined table you need to split: {"columns":["0","1","0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],"data":[["Serum sodium","101 mEq \/ L","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features."],["Serum potassium","4 mEq \/ L","Clinicopathological features","No. of patients ( n )","Relative gankyrin expression High","Relative gankyrin expression High","T","P"],["Serum chloride","78 mEq \/ L","Age",null,null,null,"0.785","0.435"],["Serum bicarbonate","23 mEq \/ L","945","9","5 ( 55.6 %)","4 ( 44.4 %)",null,null],["BUN","4 mg \/ dL","$ 45","68","39 ( 57.4 %)","29 ( 42.6 %)",null,null],["Serum creatinine","0.55 mg \/ dL","Gender",null,null,null,"1.898","0.062"],["Random blood glucose","135 mg \/ dL","Male","52","34 ( 65.4 %)","18 ( 34.6 %)",null,null],["Total protein","7.4 g \/ dL","Female","25","11 ( 44.0 %)","14 ( 56.0 %)",null,null],["Serum albumin","3.8 g \/ dL","Lymph node metastasis",null,null,null,"2.401","0.019"],["ALT","90 U \/ L","No.","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["AST","136 U \/ L","Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null],["ALP","153 U \/ L","Distant metastasis",null,null,null,"2.532","0.013"],["Total bilirubin","1.7 mg \/ dL","No","55","27 ( 49.1 %)","28 ( 50.9 %)",null,null],["Direct bilirubin","0.5 mg \/ dL","Yes","22","17 ( 77.3 %)","5 ( 22.7 %)",null,null],["Indirect bilirubin","1.2 mg \/ dL","Pathological grade",null,null,null,"2.557","0.084"],[null,null,"T \u2013 II","9","7 ( 77.8 %)","2 ( 22.2 %)",null,null],[null,null,"III","62","34 ( 54.8 %)","28 ( 45.2 %)",null,null],[null,null,"IV","6","3 ( 50.0 %)","3 ( 50.0 %)",null,null],[null,null,"Vascular invasion",null,null,null,"2.122","0.037"],[null,null,"No","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],[null,null,"Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{'table_1': '{"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],"data":[["Serum sodium","101 mEq \\/ L"],["Serum potassium","4 mEq \\/ L"],["Serum chloride","78 mEq \\/ L"],["Serum bicarbonate","23 mEq \\/ L"],["BUN","4 mg \\/ dL"],["Serum creatinine","0.55 mg \\/ dL"],["Random blood glucose","135 mg \\/ dL"],["Total protein","7.4 g \\/ dL"],["Serum albumin","3.8 g \\/ dL"],["ALT","90 U \\/ L"],["AST","136 U \\/ L"],["ALP","153 U \\/ L"],["Total bilirubin","1.7 mg \\/ dL"],["Direct bilirubin","0.5 mg \\/ dL"],["Indirect bilirubin","1.2 mg \\/ dL"]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],"data":[["TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features."],["Clinicopathological features","No. of patients ( n )","Relative gankyrin expression High","Relative gankyrin expression High","T","P"],["Age",null,null,null,"0.785","0.435"],["945","9","5 ( 55.6 %)","4 ( 44.4 %)",null,null],["$ 45","68","39 ( 57.4 %)","29 ( 42.6 %)",null,null],["Gender",null,null,null,"1.898","0.062"],["Male","52","34 ( 65.4 %)","18 ( 34.6 %)",null,null],["Female","25","11 ( 44.0 %)","14 ( 56.0 %)",null,null],["Lymph node metastasis",null,null,null,"2.401","0.019"],["No.","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null],["Distant metastasis",null,null,null,"2.532","0.013"],["No","55","27 ( 49.1 %)","28 ( 50.9 %)",null,null],["Yes","22","17 ( 77.3 %)","5 ( 22.7 %)",null,null],["Pathological grade",null,null,null,"2.557","0.084"],["T \\u2013 II","9","7 ( 77.8 %)","2 ( 22.2 %)",null,null],["III","62","34 ( 54.8 %)","28 ( 45.2 %)",null,null],["IV","6","3 ( 50.0 %)","3 ( 50.0 %)",null,null],["Vascular invasion",null,null,null,"2.122","0.037"],["No","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null]]}', 'table_3': nan}<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": "Tropical Storm Kristy", "col1": "\|\|\|.\|", "col2": null}, {"col0": "Tropical Storm Yolanda", "col1": "\|()\|.q", "col2": null}, {"col0": "Tropica1 5torm Ceorgette", "col1": 35.0, "col2": "qqq.()"}, {"col0": "Tropica1 5torm Ceorgette", "col1": 101.9, "col2": 995.0}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"Tropical Storm Kristy","col1":111.1,"col2":null},{"col0":"Tropical Storm Yolanda","col1":101.9,"col2":null},{"col0":"Tropical Storm Georgette","col1":35.0,"col2":999.0},{"col0":"Tropical Storm Georgette","col1":101.9,"col2":995.0}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,0,1,2],"data":[["Txox 2 : Treatment applied to high - energy transyndesmotic ankle fracture dislocation in published reports.","Publication","Syndesmosis fixation ( N \/ total N [% of total N ])","Tibial plafond fixation, N \/ total N (% of total N )","Medial malleolus fixation, N \/ total N (% of total N )",null],["Other treatments applied","Wang et al. [ 1 ]","( 1 ) No syndesmotic screw ( 12 \/ 41 [ 29.39.39.39.39.39.39.39.39.39.39.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.5","Not indicated","27 \/ 41 ( 65.9 %)",null],["( i ) Weight - bearing restrictions ( ii ) Dorsal expansion and plantarflexion starting 2 \u2013 3 weeks posttreatment","Bible et al. [ 2 ]","( i ) One syndesmotic screw ( 4 \/ 23 [ 17.4 %])( ii ) Two syndesmotic screws ( 15 \/ 23 [ 65.2 %])( iii ) Three syndesmotic screws ( 2 \/ 23 [ 8.7 %])","8 \/ 23 ( 34.8 %)","23 \/ 23 ( 100 %)",null],["Tsouz it : Clinical characteristics of high - energy transsyndesmotic ankle fracture dislocation in published reports.","Publication ; type of study","N of patients","Main causes of the injury ( N \/ total N [% of total N ])","Open fracture, N \/ total N (% of total N )","AO - OTA fracture and dislocation classification ( N \/ total N [% of total N ])"],["Associated fractures and injuries ( N \/ total N [% of total N ])","Wang et al. [ 1 ]; retrospective review of patient registry","41","( i ) Traffic accidents ( ii ) [ 125.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.1999","16 \/ 41 ( 39 %)","( i ) 44A ( 3 \/ 41 [ 7.3 %])( ii ) 44B ( 5 \/ 41 [ 12.2 %])( iii ) 44C ( 33 \/ 41 [ 80.5 %])"],["( i ) Distal tibiofilmular ( ii ) Distance ( ii ) Central ( 20.7 %)( ii ) Triangular Igament ( ii ) Triangular Igament ( iii ) Fibula Fracture ( iv ) Medial poollectis ( v ) Medial poollectis ( v ) feacture ( 29 \/ 41 [ 70.7 %)( iii ) injury [ 10 \/ 41 [ 24.4 %]","Bible et al. [ 2 ]; prospective cohort","23","( 1 ) Traffic accidents ( 10 \/ 23 [ 43.5 %])( ii ) Falls from an elevation ( 10 \/ 23 [ 43.5 %])( unspecified ) crush ( 3 \/ 23 [ 13 %])","12 \/ 23 ( 52.2 %)","44B ( 23 \/ 23 [ 100 %])"]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{'table_1': '{"columns":["0","1","2","3","4"],"index":[0,1,2],"data":[["Txox 2 : Treatment applied to high - energy transyndesmotic ankle fracture dislocation in published reports.","Publication","Syndesmosis fixation ( N \\/ total N [% of total N ])","Tibial plafond fixation, N \\/ total N (% of total N )","Medial malleolus fixation, N \\/ total N (% of total N )"],["Other treatments applied","Wang et al. [ 1 ]","( 1 ) No syndesmotic screw ( 12 \\/ 41 [ 29.39.39.39.39.39.39.39.39.39.39.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.5","Not indicated","27 \\/ 41 ( 65.9 %)"],["( i ) Weight - bearing restrictions ( ii ) Dorsal expansion and plantarflexion starting 2 \\u2013 3 weeks posttreatment","Bible et al. [ 2 ]","( i ) One syndesmotic screw ( 4 \\/ 23 [ 17.4 %])( ii ) Two syndesmotic screws ( 15 \\/ 23 [ 65.2 %])( iii ) Three syndesmotic screws ( 2 \\/ 23 [ 8.7 %])","8 \\/ 23 ( 34.8 %)","23 \\/ 23 ( 100 %)"]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2],"data":[["Tsouz it : Clinical characteristics of high - energy transsyndesmotic ankle fracture dislocation in published reports.","Publication ; type of study","N of patients","Main causes of the injury ( N \\/ total N [% of total N ])","Open fracture, N \\/ total N (% of total N )","AO - OTA fracture and dislocation classification ( N \\/ total N [% of total N ])"],["Associated fractures and injuries ( N \\/ total N [% of total N ])","Wang et al. [ 1 ]; retrospective review of patient registry","41","( i ) Traffic accidents ( ii ) [ 125.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.1999","16 \\/ 41 ( 39 %)","( i ) 44A ( 3 \\/ 41 [ 7.3 %])( ii ) 44B ( 5 \\/ 41 [ 12.2 %])( iii ) 44C ( 33 \\/ 41 [ 80.5 %])"],["( i ) Distal tibiofilmular ( ii ) Distance ( ii ) Central ( 20.7 %)( ii ) Triangular Igament ( ii ) Triangular Igament ( iii ) Fibula Fracture ( iv ) Medial poollectis ( v ) Medial poollectis ( v ) feacture ( 29 \\/ 41 [ 70.7 %)( iii ) injury [ 10 \\/ 41 [ 24.4 %]","Bible et al. [ 2 ]; prospective cohort","23","( 1 ) Traffic accidents ( 10 \\/ 23 [ 43.5 %])( ii ) Falls from an elevation ( 10 \\/ 23 [ 43.5 %])( unspecified ) crush ( 3 \\/ 23 [ 13 %])","12 \\/ 23 ( 52.2 %)","44B ( 23 \\/ 23 [ 100 %])"]]}', 'table_3': nan}<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"col0": null, "col1": null}, {"col0": null, "col1": 120}, {"col0": "thorium-209", "col1": 119}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"col0":"thorium-211","col1":121},{"col0":"thorium-210","col1":120},{"col0":"thorium-209","col1":119}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_6: nan table_piece_9: nan table_piece_4: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4],"data":[["Measurements","Standard error","AD","Measurement","Chromosome"],[null,"Expression","Material","LR","Month"],["Poor","Average","Cohort","Cohort",null],["MS","Not specified","Height m","< 0.00001","Body mass index"],["Code","Code","Code","Code","Code"]]} table_piece_7: nan table_piece_5: nan table_piece_8: nan table_piece_2: {"columns":["5","6"],"index":[0,1,2,3,4],"data":[["Parameter","Characteristics"],["Clone","Clone"],["< 0.0001","\u2212 0.2"],["Occupation","\u2212 0.2"],["\u03b2","\u2212 0.2"]]} table_piece_3: nan<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6" ], "index": [ 0, 1, 2, 3, 4 ], "data": [ [ "Measurements", "Standard error", "AD", "Measurement", "Chromosome", "Parameter", "Characteristics" ], [ null, "Expression", "Material", "LR", "Month", "Clone", "Clone" ], [ "Poor", "Average", "Cohort", "Cohort", null, "< 0.0001", "− 0.2" ], [ "MS", "Not specified", "Height m", "< 0.00001", "Body mass index", "Occupation", "− 0.2" ], [ "Code", "Code", "Code", "Code", "Code", "β", "− 0.2" ] ] }, "table_2": null, "table_3": null }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. Merge Fragments: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. Output the Result in JSON Format: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>These are the pieces you need to check: table_piece_9: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[8,9,10,11,12,13,14,15,16,17,18],"data":[["Item 15.","932228","214935",null,"31687","801622","801622","801622","384816","338259"],["Market for Registrant \u2019 s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities","932228","450052","633378","17422","307395",null,"616316","856009","593988"],["Amortization","258438","778485","758063","152585","896258","268629","582377","718773","764858"],["001 - 32205","988134","891006","238178","854862","581890","355850","37042","445098","895957"],["Amounts in thousands","965724","868345","104666","486081","683964","52168",null,"979694","686189"],["Amounts in thousands","849220","849220","849220","849220","849220","849220","849220","340411","282216"],["Amounts in thousands","565638","504161","504161","504161","209442",null,"124084","381020","342218"],["Amounts in thousands","211073","211073","174668","47999","200779","408552",null,"852366","363885"],["Amounts in thousands","943642","943642","943642","943642","281284","136662","438459","213770","277976"],["Amounts in thousands","302153","302153","302153",null,"344895","19403","22062","604415","203715"],["Amounts in thousands","302153","302153","302153","843803","215265","737912","687377","530202","236850"]]} table_piece_4: {"columns":["0"],"index":[0,1,2,3,4,5,6,7],"data":[["Insurance"],["Israel"],["Depreciation and amortization"],["Weighted Average Exercise Price"],["Midwest"],["Restructuring charges"],["Restructuring charges"],["Income tax expense benefit"]]} table_piece_8: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[2,3,4,5,6,7],"data":[["TOTAL","243311","277431","215576","825530","754338","432756","432756","432756","432756"],["Financial Services","484958","56801","114994","740092","824369","432756","432756","432756","432756"],["Accounts payable and accrued expenses","597129","71151","630490","630490","630490","630490","630490","630490","281859"],["AmortizedCost","445858","592799","984694","907152","504255","883865","531361","664263",null],["March 31,","932228","531553","534671","564823","345104","898437",null,"888366","805798"],["Total deposits","932228","638855","950321","216356","556570","993264","817752","315888","547653"]]} table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8],"data":[["Female","n %"],["Number","p - value"],["p value","p value"],["Parameter","10"],["Female","40"],["Mean \u00b1 SD",null],["Mean \u00b1 SD",null],["Mean \u00b1 SD",null],["p Value",null]]} table_piece_3: {"columns":["7","8","9","10"],"index":[0,1,2,3,4,5,6,7,8],"data":[[null,null,"Case","Reference"],["Variables","95 % CI","No.","Characteristic"],["Characteristics","Characteristics","Characteristics","Characteristics"],[null,null,null,null],[null,null,null,null],[null,null,null,null],[null,null,"31",null],[null,null,null,null],[null,null,"40",null]]} table_piece_7: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[0,1],"data":[["As of December 31,","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt"],["Item 5.","256712",null,"921765","649620",null,"896325","665176","791054","984568"]]} table_piece_6: {"columns":["2","3","4"],"index":[0,1,2,3,4,5,6,7],"data":[[null,"Net income","Outstanding at end of year"],["5109.40 $","98839.56 $","82214.37 $"],["3084.62 $",null,"99960.66 $"],["11515.59 $",null,"59746.33 $"],["63611.50 $",null,"43386.36 $"],["Restructuring charges","Restructuring charges","Restructuring charges"],["Restructuring charges","Restructuring charges","Restructuring charges"],[null,"51100.42 $","50373.22 $"]]} table_piece_5: {"columns":["1"],"index":[0,1,2,3,4,5,6,7],"data":[["Provision for credit losses"],[null],["48648.38 $"],["99790.31 $"],["84928.75 $"],["Restructuring charges"],["Restructuring charges"],["78.39 $"]]} table_piece_2: {"columns":["2","3","4","5","6"],"index":[0,1,2,3,4,5,6,7,8],"data":[["Frequency","Male","Name","Location","95 % CI"],["Case","p Value","Sensitivity","p Value","Number"],["p value","p value","Characteristics","Characteristics","Characteristics"],[null,null,null,null,null],["79",null,null,"20",null],[null,"70","40",null,null],[null,null,null,null,null],[null,null,null,null,null],[null,null,null,null,null]]}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ], "data": [ [ "Female", "n %", "Frequency", "Male", "Name", "Location", "95 % CI", null, null, "Case", "Reference" ], [ "Number", "p - value", "Case", "p Value", "Sensitivity", "p Value", "Number", "Variables", "95 % CI", "No.", "Characteristic" ], [ "p value", "p value", "p value", "p value", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics" ], [ "Parameter", "10", null, null, null, null, null, null, null, null, null ], [ "Female", "40", "79", null, null, "20", null, null, null, null, null ], [ "Mean ± SD", null, null, "70", "40", null, null, null, null, null, null ], [ "Mean ± SD", null, null, null, null, null, null, null, null, "31", null ], [ "Mean ± SD", null, null, null, null, null, null, null, null, null, null ], [ "p Value", null, null, null, null, null, null, null, null, "40", null ] ] }, "table_2": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "data": [ [ "Insurance", "Provision for credit losses", null, "Net income", "Outstanding at end of year" ], [ "Israel", null, "5109.40 $", "98839.56 $", "82214.37 $" ], [ "Depreciation and amortization", "48648.38 $", "3084.62 $", null, "99960.66 $" ], [ "Weighted Average Exercise Price", "99790.31 $", "11515.59 $", null, "59746.33 $" ], [ "Midwest", "84928.75 $", "63611.50 $", null, "43386.36 $" ], [ "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges" ], [ "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges" ], [ "Income tax expense benefit", "78.39 $", null, "51100.42 $", "50373.22 $" ] ] }, "table_3": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 ], "data": [ [ "As of December 31,", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt" ], [ "Item 5.", "256712", null, "921765", "649620", null, "896325", "665176", "791054", "984568" ], [ "TOTAL", "243311", "277431", "215576", "825530", "754338", "432756", "432756", "432756", "432756" ], [ "Financial Services", "484958", "56801", "114994", "740092", "824369", "432756", "432756", "432756", "432756" ], [ "Accounts payable and accrued expenses", "597129", "71151", "630490", "630490", "630490", "630490", "630490", "630490", "281859" ], [ "AmortizedCost", "445858", "592799", "984694", "907152", "504255", "883865", "531361", "664263", null ], [ "March 31,", "932228", "531553", "534671", "564823", "345104", "898437", null, "888366", "805798" ], [ "Total deposits", "932228", "638855", "950321", "216356", "556570", "993264", "817752", "315888", "547653" ], [ "Item 15.", "932228", "214935", null, "31687", "801622", "801622", "801622", "384816", "338259" ], [ "Market for Registrant ’ s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities", "932228", "450052", "633378", "17422", "307395", null, "616316", "856009", "593988" ], [ "Amortization", "258438", "778485", "758063", "152585", "896258", "268629", "582377", "718773", "764858" ], [ "001 - 32205", "988134", "891006", "238178", "854862", "581890", "355850", "37042", "445098", "895957" ], [ "Amounts in thousands", "965724", "868345", "104666", "486081", "683964", "52168", null, "979694", "686189" ], [ "Amounts in thousands", "849220", "849220", "849220", "849220", "849220", "849220", "849220", "340411", "282216" ], [ "Amounts in thousands", "565638", "504161", "504161", "504161", "209442", null, "124084", "381020", "342218" ], [ "Amounts in thousands", "211073", "211073", "174668", "47999", "200779", "408552", null, "852366", "363885" ], [ "Amounts in thousands", "943642", "943642", "943642", "943642", "281284", "136662", "438459", "213770", "277976" ], [ "Amounts in thousands", "302153", "302153", "302153", null, "344895", "19403", "22062", "604415", "203715" ], [ "Amounts in thousands", "302153", "302153", "302153", "843803", "215265", "737912", "687377", "530202", "236850" ] ] } }<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Britvic"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "1998-01-01T00:00:00Z"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Britvic"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"1998-01-01T00:00:00Z"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "paprik\u00e1s"}, {"Prop0": "PropZ", "specific food preparation with cooking finished, and ready to eat, or be served": "Hungary"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Hungarian cuisine"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "meat, Allium, smetana, paprika"}, {"Prop0": "Prop5", "specific food preparation with cooking finished, and ready to eat, or be served": "paprykarz szczeci\u0144ski"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"paprikás"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Hungary"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Hungarian cuisine"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"meat, Allium, smetana, paprika"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"paprykarz szczeciński"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "food, edible in the raw state": "Rhus integrifolia"}, {"Prop0": "Prop2", "food, edible in the raw state": "R3MW7M4V9D"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","food, edible in the raw state":"Rhus integrifolia"},{"Prop0":"Prop2","food, edible in the raw state":"R3MW7M4V9D"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop\|", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "PropZ", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "table salt"}, {"Prop0": "Prop3", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"table salt"},{"Prop0":"Prop3","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Sjus"}, {"Prop0": null, "kind of liquid which is specifically prepared for human consumption": null}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Sjus"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"alcohols, soft drink"}]<\|eot_id\|>
<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `\|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "\|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`\|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "\|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>[{"Prop0": "Prop\|", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "PropZ", "type of dish comprised of fish": "India"}]<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<\|eot_id\|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "pasta, milk, cheese, potato, Allium cepa, butter, mushroom, egg, sausage, smoked meat"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Europe, Indonesia"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"pasta, milk, cheese, potato, Allium cepa, butter, mushroom, egg, sausage, smoked meat"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Europe, Indonesia"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_9: nan table_piece_4: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12],"data":[["Baseline","n","Gene name","Sequence","Gene","Name"],["OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI","OR 95 % CI"],["95 % CI","41",null,null,null,null],["Sample",null,null,null,null,null],["References","33",null,null,null,null],["t",null,null,null,null,"48"],["Control",null,null,null,"14","44"],["Sequence",null,"35",null,null,null],["Gene name",null,null,null,"72",null],["Case",null,"13","88",null,null],["Category","89",null,null,null,null],["SE",null,null,"87",null,"7"],["HR",null,"43",null,"33","21"]]} table_piece_5: {"columns":["0","1","2","3","4","5"],"index":[13,14,15,16,17,18,19,20,21,22],"data":[["Gene",null,null,null,"39","50"],["t",null,"36",null,null,null],["N %",null,"64",null,"10",null],["N %",null,null,null,null,null],["Location",null,null,null,null,null],["Genotype",null,null,null,"29",null],["Sample",null,null,null,null,null],["Variable",null,null,"7",null,null],["p Value",null,null,null,"52",null],["p - Value",null,null,null,null,null]]} table_piece_3: nan table_piece_7: {"columns":["0","1","2","3"],"index":[0,1,2,3,4,5,6,7],"data":[["Country","Treatment","Treatment","Controls"],[null,null,null,null],["Female",null,null,null],["Source","25",null,null],["P",null,"0",null],[null,null,"55",null],["A",null,null,null],[null,null,"38",null]]} table_piece_1: {"columns":["0","1","2","3","4","5","6","7","8"],"index":[0,1,2,3],"data":[["Specificity","p","No.","Case","Region","Reference","Case","Case","Case"],["Characteristic",null,null,null,null,null,null,null,"54"],["Species",null,"24","2",null,null,null,null,null],[null,null,null,null,null,null,null,"25",null]]} table_piece_8: {"columns":["4","5","6","7"],"index":[0,1,2,3,4,5,6,7],"data":[["F","M","p - value","SD"],[null,null,"16",null],[null,null,null,null],[null,null,null,null],[null,null,null,"95"],[null,"49",null,null],[null,"64",null,null],[null,"72",null,null]]} table_piece_6: nan table_piece_2: {"columns":["0","1","2","3","4","5","6","7","8"],"index":[4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"data":[["p Value","25","48",null,null,null,null,null,null],[null,"0",null,null,null,null,"79",null,null],["Total",null,null,null,null,null,null,null,null],["M",null,null,null,null,"33",null,null,null],[null,null,null,null,null,null,null,null,null],["Name",null,null,null,null,null,null,null,null],["Mean \u00b1 SD",null,null,"37",null,null,null,null,null],["Name","99",null,null,null,"60",null,null,null],["Patient",null,"42","1",null,"47",null,null,null],["Patient",null,null,null,null,null,"92",null,null],["Median",null,"15",null,"60","71",null,"33",null],["All",null,null,null,null,"91",null,null,null],["Name",null,"90","36",null,"53",null,"99",null],[null,null,"98",null,null,null,null,null,null],["Female",null,"41",null,null,null,null,null,null],["Patient","14",null,null,"98",null,null,null,null],["Gene name",null,null,null,null,"71",null,null,null],["N %",null,null,"41",null,null,null,"25",null],["Sex",null,"54",null,null,null,"75",null,null],["P value",null,null,"60","70",null,null,null,null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "data": [ [ "Specificity", "p", "No.", "Case", "Region", "Reference", "Case", "Case", "Case" ], [ "Characteristic", null, null, null, null, null, null, null, "54" ], [ "Species", null, "24", "2", null, null, null, null, null ], [ null, null, null, null, null, null, null, "25", null ], [ "p Value", "25", "48", null, null, null, null, null, null ], [ null, "0", null, null, null, null, "79", null, null ], [ "Total", null, null, null, null, null, null, null, null ], [ "M", null, null, null, null, "33", null, null, null ], [ null, null, null, null, null, null, null, null, null ], [ "Name", null, null, null, null, null, null, null, null ], [ "Mean ± SD", null, null, "37", null, null, null, null, null ], [ "Name", "99", null, null, null, "60", null, null, null ], [ "Patient", null, "42", "1", null, "47", null, null, null ], [ "Patient", null, null, null, null, null, "92", null, null ], [ "Median", null, "15", null, "60", "71", null, "33", null ], [ "All", null, null, null, null, "91", null, null, null ], [ "Name", null, "90", "36", null, "53", null, "99", null ], [ null, null, "98", null, null, null, null, null, null ], [ "Female", null, "41", null, null, null, null, null, null ], [ "Patient", "14", null, null, "98", null, null, null, null ], [ "Gene name", null, null, null, null, "71", null, null, null ], [ "N %", null, null, "41", null, null, null, "25", null ], [ "Sex", null, "54", null, null, null, "75", null, null ], [ "P value", null, null, "60", "70", null, null, null, null ] ] }, "table_2": { "columns": [ "0", "1", "2", "3", "4", "5" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 ], "data": [ [ "Baseline", "n", "Gene name", "Sequence", "Gene", "Name" ], [ "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI", "OR 95 % CI" ], [ "95 % CI", "41", null, null, null, null ], [ "Sample", null, null, null, null, null ], [ "References", "33", null, null, null, null ], [ "t", null, null, null, null, "48" ], [ "Control", null, null, null, "14", "44" ], [ "Sequence", null, "35", null, null, null ], [ "Gene name", null, null, null, "72", null ], [ "Case", null, "13", "88", null, null ], [ "Category", "89", null, null, null, null ], [ "SE", null, null, "87", null, "7" ], [ "HR", null, "43", null, "33", "21" ], [ "Gene", null, null, null, "39", "50" ], [ "t", null, "36", null, null, null ], [ "N %", null, "64", null, "10", null ], [ "N %", null, null, null, null, null ], [ "Location", null, null, null, null, null ], [ "Genotype", null, null, null, "29", null ], [ "Sample", null, null, null, null, null ], [ "Variable", null, null, "7", null, null ], [ "p Value", null, null, null, "52", null ], [ "p - Value", null, null, null, null, null ] ] }, "table_3": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "data": [ [ "Country", "Treatment", "Treatment", "Controls", "F", "M", "p - value", "SD" ], [ null, null, null, null, null, null, "16", null ], [ "Female", null, null, null, null, null, null, null ], [ "Source", "25", null, null, null, null, null, null ], [ "P", null, "0", null, null, null, null, "95" ], [ null, null, "55", null, null, "49", null, null ], [ "A", null, null, null, null, "64", null, null ], [ null, null, "38", null, null, "72", null, null ] ] } }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Mulo diye msur dal"}, {"Prop0": "Prop3", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Mulo diye musur dal"},{"Prop0":"Prop3","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "0RFA()", "col1": "Category:Kiwifruit", "col2": "()q44S, ()q|4&, ()q|4&, ()q|4&, ()q|4&, ()q|4&, ()q|4&, ()q|4&, ()q|4&"}, {"col0": "OKEA1", "col1": "Category:Nephelium lappaceum", "col2": "()q3()|"}, {"col0": "OKEA2", "col1": "Gategory:AVocado", "col2": "09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038"}, {"col0": "OKEA3", "col1": "Gategory:Mangoes", "col2": "()q|7G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G, ()q43G"}, {"col0": "OKEA4", "col1": "Gategory:0ranges (fruit)", "col2": "09202, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216"}, {"col0": "OKEA5", "col1": "Gategory:Crapefruit", "col2": "()q||&, ()q||Z, ()q||7, ()q||3, ()q||4, ()q|Z(), ()q||q, ()q|||, ()q||G, ()q|Z|, ()q||Z, ()q||7, ()q||3, ()q||4, ()q|Z(), ()q||q, ()q|||, ()q||G, ()q|Z|, ()q||Z, ()q||7, ()q||3, ()q||4, ()q|Z(), ()q||q, ()q|||, ()q||G, ()q|Z|, ()q||Z, ()q||7, ()q||3, ()q||4, ()q|Z(), ()q||q, ()q|||, ()q||G, ()q|Z|, ()q||Z, ()q||7, ()q||3, ()q||4, ()q|Z(), ()q||q, ()q|||, ()q||G, ()q|Z|"}, {"col0": "OKEA6", "col1": "Gategory:Pineapp1es", "col2": "()q4Zq, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4, ()q43(), ()qZG&, ()qZGG, ()qZ73, ()qZ7(), ()qZGq, ()qZG7, ()qZ7|, ()qZ7Z, ()q3S4"}, {"col0": "OKEA7", "col1": "Category:Cherries", "col2": "09070, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076"}, {"col0": "0RFA&", "col1": "Gategory:01iVes", "col2": "09194, 09193, 09195, 09193, 09195"}, {"col0": "0RFAq", "col1": "Gategory:8ananas", "col2": "09040, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041"}, {"col0": "0RFA|()", "col1": "Category:Limes (fruit)", "col2": "09159, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160"}, {"col0": "OKEA11", "col1": "Category:Apricots", "col2": "()q()Z4, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&, ()q()3|, ()q()3S, ()q()ZG, ()q4()3, ()q()3Z, ()q()ZS, ()q()3(), ()q()ZZ, ()q()33, ()q()Z3, ()q3S7, ()q()Z|, ()q()3G, ()q()Zq, ()q()Z7, ()q()Z&"}, {"col0": "0RFA|Z", "col1": "Gategory:App1es", "col2": "()q()()S, ()q()()7, ()q()||, ()q()|S, ()q()|4, ()q()|(), ()q()|Z, ()q()()3, ()q()()&, ()q()|3, ()q()()4, ()q()()G, ()q()()q, ()q()()7, ()q()||, ()q()|S, ()q()|4, ()q()|(), ()q()|Z, ()q()()3, ()q()()&, ()q()|3, ()q()()4, ()q()()G, ()q()()q, ()q()()7, ()q()||, ()q()|S, ()q()|4, ()q()|(), ()q()|Z, ()q()()3, ()q()()&, ()q()|3, ()q()()4, ()q()()G, ()q()()q, ()q()()7, ()q()||, ()q()|S, ()q()|4, ()q()|(), ()q()|Z, ()q()()3, ()q()()&, ()q()|3, ()q()()4, ()q()()G, ()q()()q, ()q()()7, ()q()||, ()q()|S, ()q()|4, ()q()|(), ()q()|Z, ()q()()3, ()q()()&, ()q()|3, ()q()()4, ()q()()G, ()q()()q"}, {"col0": "0RFA|3", "col1": "Category:Ribes", "col2": "09084"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"OKEA0","col1":"Category:Kiwifruit","col2":"09445, 09148, 09148, 09148, 09148, 09148, 09148, 09148, 09148"},{"col0":"OKEA1","col1":"Category:Nephelium lappaceum","col2":"09301"},{"col0":"OKEA2","col1":"Category:Avocado","col2":"09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038"},{"col0":"OKEA3","col1":"Category:Mangoes","col2":"09176, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436"},{"col0":"OKEA4","col1":"Category:Oranges (fruit)","col2":"09202, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216, 09203, 09205, 85000, 09201, 09216"},{"col0":"OKEA5","col1":"Category:Grapefruit","col2":"09118, 09112, 09117, 09113, 09114, 09120, 09119, 09111, 09116, 09121, 09112, 09117, 09113, 09114, 09120, 09119, 09111, 09116, 09121, 09112, 09117, 09113, 09114, 09120, 09119, 09111, 09116, 09121, 09112, 09117, 09113, 09114, 09120, 09119, 09111, 09116, 09121, 09112, 09117, 09113, 09114, 09120, 09119, 09111, 09116, 09121"},{"col0":"OKEA6","col1":"Category:Pineapples","col2":"09429, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354, 09430, 09268, 09266, 09273, 09270, 09269, 09267, 09271, 09272, 09354"},{"col0":"OKEA7","col1":"Category:Cherries","col2":"09070, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076"},{"col0":"OKEA8","col1":"Category:Olives","col2":"09194, 09193, 09195, 09193, 09195"},{"col0":"OKEA9","col1":"Category:Bananas","col2":"09040, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041, 09446, 09447, 09041"},{"col0":"OKEA10","col1":"Category:Limes (fruit)","col2":"09159, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160"},{"col0":"OKEA11","col1":"Category:Apricots","col2":"09024, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028, 09031, 09035, 09026, 09403, 09032, 09025, 09030, 09022, 09033, 09023, 09357, 09021, 09036, 09029, 09027, 09028"},{"col0":"OKEA12","col1":"Category:Apples","col2":"09005, 09007, 09011, 09015, 09014, 09010, 09012, 09003, 09008, 09013, 09004, 09006, 09009, 09007, 09011, 09015, 09014, 09010, 09012, 09003, 09008, 09013, 09004, 09006, 09009, 09007, 09011, 09015, 09014, 09010, 09012, 09003, 09008, 09013, 09004, 09006, 09009, 09007, 09011, 09015, 09014, 09010, 09012, 09003, 09008, 09013, 09004, 09006, 09009, 09007, 09011, 09015, 09014, 09010, 09012, 09003, 09008, 09013, 09004, 09006, 09009"},{"col0":"OKEA13","col1":"Category:Ribes","col2":"09084"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Ben"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <|eot_id|><|start_header_id|>user<|end_header_id|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5","6","7","8","9","10","0","1","2","3","0","1","2","3","4","5","6","7","8","9"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","N \/ A","California","Cash provided by operating activities","Noninterest income","Total loans","8 - K","8 - K","Properties","Deferred","TX","Current portion of long - term debt","a","Less : Net income attributable to noncontrolling interests","Receivables","Receivables","Receivables","Receivables","Receivables","Receivables"],[null,"Deferred tax liabilities :","Total liabilities and shareholders \u2019 equity","Other Benefits","Retained Earnings","F - 5","Other income expense","Current","Current","Current","% of Total","Distribution Center","0.11 %","76.58 %","51.71 %","A3","Current portion of long - term debt","Entergy Louisiana","Period","Other expense, net","Accounts payable","Average interest rate","Average interest rate","Average interest rate","Comprehensive income"],["Total consumer","98582.65 $","7657.56 $","15622.65 $",null,"62660.86 $",null,"66362.2 $","26381.11 $","59640.56 $","59640.56 $",null,"34.91 %","32.95 %","67.31 %",null,"Current portion of long - term debt",null,"Eliminations","Eliminations","Eliminations","Average interest rate","Average interest rate","Average interest rate","Construction in progress"],["in millions","52361.25 $","76119.68 $","15622.65 $","41527.17 $","89592.61 $","87726.74 $","16529.89 $","72576.23 $","52887.91 $","32729.75 $","Thereafter","65.16 %","27.88 %","41.10 %","Industrial","48.71 %","98.61 %","37.89 %",null,"40.67 %","47.86 %",null,"68.96 %","17.59 %"],["Balance, beginning of period","89992.16 $","98263.49 $","45856.35 $","39151.56 $","99174.62 $","21302.86 $","88692.60 $","49973.62 $","5553.74 $",null,"Income from continuing operations before income taxes","64.2 %","66.48 %","66.48 %","Interest paid","85.93 %","97.83 %","97.83 %","48.5 %","40.67 %","86.74 %","63.3 %","65.62 %","30.32 %"],["Total costs and expenses","20028.66 $","64455.70 $","80157.53 $",null,"72220.70 $","68872.28 $","61785.11 $","99079.45 $","38007.18 $","99840.38 $","Product","Product","66.48 %","66.48 %","Preferred stock dividends","84.70 %","6.69 %","90.25 %","53.80 %","38.22 %","99.4 %","53.82 %","25.48 %","18.15 %"],["Euro","24067.59 $","75731.69 $","80157.53 $",null,"20474.39 $","16743.64 $","82057.80 $","5530.5 $","48327.31 $","56947.64 $","Baa1","17.98 %","20.19 %","7.40 %","Expenses","18.33 %","18.33 %","18.33 %","80.55 %","32.42 %","94.46 %","47.69 %","53.47 %","18.15 %"],["ASSETS","20008.20 $","20008.20 $","20008.20 $",null,"74560.12 $","98464.87 $","66792.45 $",null,"37456.23 $","78204.56 $","Various","86.18 %",null,"7.40 %",null,"94.90 %","94.38 %",null,"80.55 %","0.34 %","0.34 %","0.34 %","0.34 %","0.34 %"],["Texas","16566.25 $","90074.81 $","29735.61 $",null,"19827.38 $",null,"34398.39 $",null,null,"69137.78 $","Location","46.31 %","46.31 %","7.40 %","June 30","17.53 %","77.69 %","89.85 %","66.57 %","16.88 %","49.40 %","12.14 %","14.1 %","85.94 %"],["Current assets :","62575.58 $","76594.23 $",null,null,"56486.2 $","56486.2 $","56486.2 $","56486.2 $","16135.26 $","11895.98 $","S - 1","46.31 %","46.31 %","85.85 %",null,"49.34 %","58.89 %","0.56 %","47.66 %","87.48 %","51.51 %","76.55 %","72.84 %","43.40 %"],["Other assets","Other assets","Other assets",null,null,"1898.4 $","99139.41 $","13978.85 $","22032.82 $","16135.26 $","52566.15 $","Benefits paid","99.85 %","94.49 %","50.12 %","Prior service credit","16.24 %","7.91 %","52.9 %",null,"6.21 %","97.69 %","48.81 %","68.75 %","98.99 %"],["Total loans and leases","33898.68 $","68041.32 $","75870.36 $",null,"61080.19 $","21091.19 $","14934.49 $","46804.58 $","97792.4 $","97792.4 $",null,null,null,null,null,null,null,null,null,null,null,null,null,null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{'table_1': '{"columns":["0","1","2","3","4","5","6","7","8","9","10"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","Exhibit Number","N \\/ A","California","Cash provided by operating activities","Noninterest income","Total loans"],[null,"Deferred tax liabilities :","Total liabilities and shareholders \\u2019 equity","Other Benefits","Retained Earnings","F - 5","Other income expense","Current","Current","Current","% of Total"],["Total consumer","98582.65 $","7657.56 $","15622.65 $",null,"62660.86 $",null,"66362.2 $","26381.11 $","59640.56 $","59640.56 $"],["in millions","52361.25 $","76119.68 $","15622.65 $","41527.17 $","89592.61 $","87726.74 $","16529.89 $","72576.23 $","52887.91 $","32729.75 $"],["Balance, beginning of period","89992.16 $","98263.49 $","45856.35 $","39151.56 $","99174.62 $","21302.86 $","88692.60 $","49973.62 $","5553.74 $",null],["Total costs and expenses","20028.66 $","64455.70 $","80157.53 $",null,"72220.70 $","68872.28 $","61785.11 $","99079.45 $","38007.18 $","99840.38 $"],["Euro","24067.59 $","75731.69 $","80157.53 $",null,"20474.39 $","16743.64 $","82057.80 $","5530.5 $","48327.31 $","56947.64 $"],["ASSETS","20008.20 $","20008.20 $","20008.20 $",null,"74560.12 $","98464.87 $","66792.45 $",null,"37456.23 $","78204.56 $"],["Texas","16566.25 $","90074.81 $","29735.61 $",null,"19827.38 $",null,"34398.39 $",null,null,"69137.78 $"],["Current assets :","62575.58 $","76594.23 $",null,null,"56486.2 $","56486.2 $","56486.2 $","56486.2 $","16135.26 $","11895.98 $"],["Other assets","Other assets","Other assets",null,null,"1898.4 $","99139.41 $","13978.85 $","22032.82 $","16135.26 $","52566.15 $"],["Total loans and leases","33898.68 $","68041.32 $","75870.36 $",null,"61080.19 $","21091.19 $","14934.49 $","46804.58 $","97792.4 $","97792.4 $"]]}', 'table_2': '{"columns":["0","1","2","3"],"index":[0,1,2,3,4,5,6,7,8,9,10],"data":[["8 - K","8 - K","Properties","Deferred"],["Distribution Center","0.11 %","76.58 %","51.71 %"],[null,"34.91 %","32.95 %","67.31 %"],["Thereafter","65.16 %","27.88 %","41.10 %"],["Income from continuing operations before income taxes","64.2 %","66.48 %","66.48 %"],["Product","Product","66.48 %","66.48 %"],["Baa1","17.98 %","20.19 %","7.40 %"],["Various","86.18 %",null,"7.40 %"],["Location","46.31 %","46.31 %","7.40 %"],["S - 1","46.31 %","46.31 %","85.85 %"],["Benefits paid","99.85 %","94.49 %","50.12 %"]]}', 'table_3': '{"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[0,1,2,3,4,5,6,7,8,9,10],"data":[["TX","Current portion of long - term debt","a","Less : Net income attributable to noncontrolling interests","Receivables","Receivables","Receivables","Receivables","Receivables","Receivables"],["A3","Current portion of long - term debt","Entergy Louisiana","Period","Other expense, net","Accounts payable","Average interest rate","Average interest rate","Average interest rate","Comprehensive income"],[null,"Current portion of long - term debt",null,"Eliminations","Eliminations","Eliminations","Average interest rate","Average interest rate","Average interest rate","Construction in progress"],["Industrial","48.71 %","98.61 %","37.89 %",null,"40.67 %","47.86 %",null,"68.96 %","17.59 %"],["Interest paid","85.93 %","97.83 %","97.83 %","48.5 %","40.67 %","86.74 %","63.3 %","65.62 %","30.32 %"],["Preferred stock dividends","84.70 %","6.69 %","90.25 %","53.80 %","38.22 %","99.4 %","53.82 %","25.48 %","18.15 %"],["Expenses","18.33 %","18.33 %","18.33 %","80.55 %","32.42 %","94.46 %","47.69 %","53.47 %","18.15 %"],[null,"94.90 %","94.38 %",null,"80.55 %","0.34 %","0.34 %","0.34 %","0.34 %","0.34 %"],["June 30","17.53 %","77.69 %","89.85 %","66.57 %","16.88 %","49.40 %","12.14 %","14.1 %","85.94 %"],[null,"49.34 %","58.89 %","0.56 %","47.66 %","87.48 %","51.51 %","76.55 %","72.84 %","43.40 %"],["Prior service credit","16.24 %","7.91 %","52.9 %",null,"6.21 %","97.69 %","48.81 %","68.75 %","98.99 %"]]}'}<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "o|el", "col1": "Velko", "col2": 7.53}, {"col0": "oh Wa", "col1": "Hohe", "col2": 4.7094}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"o|el","col1":"Velkopopovický Kozel","col2":7.53},{"col0":"oh Wand","col1":"Hohe Wand","col2":4.7094}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task: You are tasked with recovering a dataset where OCR (Optical Character Recognition) has truncated or misinterpreted some text or numerical values. The goal is to reconstruct the original table accurately by correcting damaged entries based on logical patterns or known formats. --- Input: 1. Damaged Dataset: A JSON-like structure representing the output of an OCR tool. Some of its text strings or numerical values may be truncated, split, or corrupted due to OCR errors. 2. Recovery Context: Logical patterns, specific formats, or plausible corrections that guide the reconstruction of the table. --- Output: 1. Recovered Dataset: A JSON-like structure where damaged cells are corrected based on context and patterns. --- Guidelines: 1. Text Completion: - Detect truncated or garbled text and complete it using context or domain-specific knowledge. - Ensure that names, locations, or categorical values are meaningful and plausible. 2. Numerical Correction: - Identify incomplete or split numbers and reformat them based on logical or domain-specific rules (e.g., year reconstruction, decimal precision). 3. Special Characters: - Correct or reinstate diacritics or special characters (e.g., `å`, `ä`, `ö`) as needed for proper language representation. 4. Context Awareness: - Consider patterns within the dataset. For example: - If a column represents dates, reconstruct the values to fit standard formats (e.g., ISO 8601). - If a column contains names, ensure they are plausible and follow linguistic rules. 5. Validation: - Validate the final table for logical consistency across all entries. --- Instructions: 1. Parse the input dataset to identify damaged or corrupted entries. 2. Apply contextual rules to reconstruct the damaged cells. 3. Output the recovered dataset as a JSON-like structure, ensuring all corrections align with logical or domain-specific expectations. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "S", "col1": 1.0}, {"col0": "Sam", "col1": 1.0}, {"col0": "Sams", "col1": 2.0}, {"col0": "Red", "col1": 2.0}, {"col0": "Sams", "col1": 2.0}, {"col0": "Sam", "col1": 1.5}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Samsung Galaxy J1 Ace","col1":1.0},{"col0":"Samsung Galaxy J1 mini prime","col1":1.0},{"col0":"Samsung Galaxy A10","col1":2.0},{"col0":"Redmi 7A","col1":2.0},{"col0":"Samsung Galaxy J5 Prime","col1":2.0},{"col0":"Samsung Galaxy J2 (2016)","col1":1.5}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_2: {"columns":["2"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Primer"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse"],["Extension :"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse :"],["Extension :"],["Forward"],["Reverse :"],["Extension :"]]} table_piece_4: nan table_piece_7: nan table_piece_8: nan table_piece_6: nan table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Table 2. Primer sequences used in genotyping for the Pit - 1 gene","SNP"],["Sequence ( 5 \u2032 to 3 \u2032)","rs13687126,"],[null,"ACGTTGGATGTTGAATGTGAGAGGAGAAGG"],[null,"ACGTTGGATGTCCTCTGTCAATGCCATCTC"],["AGCCCCACAATGACA","rs13687128"],[null,"ACGTTGGATGCTCAAGATTAAGCCCCTCAG"],[null,"ACGTTGGATGGGCACTTTGGAGAACAAAGC"],["AACGCCTTCTTCTCAGGAAAT","rs13905624"],[null,"ACGTTGGATGTTTATACGTAGGCACAACAG"],[null,"ACGTTGGATGTAAAGTGCCCTCTTGTGTCC"],["GCTTAAGCCAGCTTTGG,","rs13905622,"],[null,"ACGTTGGATGCAGCTGCTGAGGAAATCAAG"],[null,"ACGTTGGATGTAGCCTCAGTGTAATGAGGG"],["TGTAATGAGGGTAGATATAAAAG","rs13905628,"],[null,"ACGTTGGATGGTCTGAGAGCTACAATCGTG"],[null,"ACGTTGGATGGAGGGGAATGGTTTTTGTGG"]]} table_piece_9: nan table_piece_5: nan table_piece_3: nan<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ], "data": [ [ "Table 2. Primer sequences used in genotyping for the Pit - 1 gene", "SNP", "Primer" ], [ "Sequence ( 5 ′ to 3 ′)", "rs13687126,", "Forward" ], [ null, "ACGTTGGATGTTGAATGTGAGAGGAGAAGG", "Reverse :" ], [ null, "ACGTTGGATGTCCTCTGTCAATGCCATCTC", "Extension :" ], [ "AGCCCCACAATGACA", "rs13687128", "Forward" ], [ null, "ACGTTGGATGCTCAAGATTAAGCCCCTCAG", "Reverse" ], [ null, "ACGTTGGATGGGCACTTTGGAGAACAAAGC", "Extension :" ], [ "AACGCCTTCTTCTCAGGAAAT", "rs13905624", "Forward" ], [ null, "ACGTTGGATGTTTATACGTAGGCACAACAG", "Reverse :" ], [ null, "ACGTTGGATGTAAAGTGCCCTCTTGTGTCC", "Extension :" ], [ "GCTTAAGCCAGCTTTGG,", "rs13905622,", "Forward" ], [ null, "ACGTTGGATGCAGCTGCTGAGGAAATCAAG", "Reverse :" ], [ null, "ACGTTGGATGTAGCCTCAGTGTAATGAGGG", "Extension :" ], [ "TGTAATGAGGGTAGATATAAAAG", "rs13905628,", "Forward" ], [ null, "ACGTTGGATGGTCTGAGAGCTACAATCGTG", "Reverse :" ], [ null, "ACGTTGGATGGAGGGGAATGGTTTTTGTGG", "Extension :" ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Le8ron James", "col1": "St. Vincent\u2013St. Mary High School", "col2": "Dom James"}, {"col0": "A1i", "col1": "Central City High School", "col2": "Lita"}, {"col0": "Tony 5tark", "col1": "Massachusetts Institute of Technology", "col2": "Morgan Stark"}, {"col0": "Xue Pan", "col1": "Jia's Family School", "col2": "son of Xiangling"}, {"col0": "Phi1ip 8anks", "col1": "Princeton University", "col2": "Ashley Banks"}, {"col0": "Yukiko Kudo", "col1": "Teitan Elementary School", "col2": "Gonan FdogaWa"}, {"col0": "Josiah Bartlet", "col1": "Dartmouth Go11ege", "col2": "Eleanor Bartlet"}, {"col0": "Wa1ter 8ishop", "col1": "Harvard University", "col2": "Peter Bishop"}, {"col0": "Batman", "col1": "University of Cambridge", "col2": "Cassandra Cain"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"LeBron James","col1":"St. Vincent–St. Mary High School","col2":"Dom James"},{"col0":"Ali","col1":"Central City High School","col2":"Lita"},{"col0":"Tony Stark","col1":"Massachusetts Institute of Technology","col2":"Morgan Stark"},{"col0":"Xue Pan","col1":"Jia's Family School","col2":"son of Xiangling"},{"col0":"Philip Banks","col1":"Princeton University","col2":"Ashley Banks"},{"col0":"Yukiko Kudo","col1":"Teitan Elementary School","col2":"Conan Edogawa"},{"col0":"Josiah Bartlet","col1":"Dartmouth College","col2":"Eleanor Bartlet"},{"col0":"Walter Bishop","col1":"Harvard University","col2":"Peter Bishop"},{"col0":"Batman","col1":"University of Cambridge","col2":"Cassandra Cain"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_6: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4,5,6,7,8],"data":[["95 % CI",null,"SE","Name","Method"],["Male","Value","Range","\u03b2","Method"],["Mean SD","Female","Source","Control","Method"],["B",null,null,null,null],["All",null,null,null,null],[null,"1",null,"49","90"],["Genotype","51",null,null,null],["Sensitivity",null,null,null,null],["Study",null,null,null,null]]} table_piece_7: nan table_piece_9: nan table_piece_4: nan table_piece_8: nan table_piece_3: nan table_piece_2: {"columns":["0","1","2","3","4"],"index":[9,10,11,12],"data":[["B","81",null,null,null],["Number",null,"75","26",null],["Location",null,"70","34",null],[null,null,"93",null,null]]} table_piece_5: nan<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ], "data": [ [ "95 % CI", null, "SE", "Name", "Method" ], [ "Male", "Value", "Range", "β", "Method" ], [ "Mean SD", "Female", "Source", "Control", "Method" ], [ "B", null, null, null, null ], [ "All", null, null, null, null ], [ null, "1", null, "49", "90" ], [ "Genotype", "51", null, null, null ], [ "Sensitivity", null, null, null, null ], [ "Study", null, null, null, null ], [ "B", "81", null, null, null ], [ "Number", null, "75", "26", null ], [ "Location", null, "70", "34", null ], [ null, null, "93", null, null ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_3: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4],"data":[["Cases",null,null,null,null],["Mean","Median","References","C","Baseline"],["Sensitivity",null,"31","92",null],["Frequency",null,null,null,null],["p - value",null,null,null,null]]} table_piece_2: {"columns":["0","1","2","3","4"],"index":[5,6,7,8,9,10,11,12,13,14],"data":[["Location",null,null,null,null],["Percentage","88",null,"75",null],["No",null,null,"53","45"],["Gene",null,null,null,"1"],["Category",null,null,null,null],["Location",null,null,null,null],[null,null,null,null,null],["Baseline",null,null,null,null],["Male",null,null,null,"51"],["Source",null,null,null,null]]} table_piece_7: nan table_piece_5: nan table_piece_9: nan table_piece_8: nan table_piece_4: nan table_piece_6: nan<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ], "data": [ [ "Cases", null, null, null, null ], [ "Mean", "Median", "References", "C", "Baseline" ], [ "Sensitivity", null, "31", "92", null ], [ "Frequency", null, null, null, null ], [ "p - value", null, null, null, null ], [ "Location", null, null, null, null ], [ "Percentage", "88", null, "75", null ], [ "No", null, null, "53", "45" ], [ "Gene", null, null, null, "1" ], [ "Category", null, null, null, null ], [ "Location", null, null, null, null ], [ null, null, null, null, null ], [ "Baseline", null, null, null, null ], [ "Male", null, null, null, "51" ], [ "Source", null, null, null, null ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop|", "specific food preparation with cooking finished, and ready to eat, or be served": "Poland"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "uszka"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Belarusian cuisine"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "Russian cuisine, Polish cuisine, Ukrainian cuisine, Lithuanian cuisine"}, {"Prop0": "PropS", "specific food preparation with cooking finished, and ready to eat, or be served": "Meyers Konversations-Lexikon, 4th edition (1885\u20131890)"}, {"Prop0": "PropG", "specific food preparation with cooking finished, and ready to eat, or be served": "dough, edib1e mushroom, minced meat"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "Uszka"}, {"Prop0": "Prop8", "specific food preparation with cooking finished, and ready to eat, or be served": "Uszka"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Poland"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"uszka"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Belarusian cuisine"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"Russian cuisine, Polish cuisine, Ukrainian cuisine, Lithuanian cuisine"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Meyers Konversations-Lexikon, 4th edition (1885–1890)"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"dough, edible mushroom, minced meat"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"Uszka"},{"Prop0":"Prop8","specific food preparation with cooking finished, and ready to eat, or be served":"Uszka"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <|eot_id|><|start_header_id|>user<|end_header_id|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,0,1,2,3,4,5,6,7,8,9,10,11],"data":[[null,"Specificity","Specificity",null,null,null],[null,null,null,null,null,null],["Gender",null,null,null,null,null],["Characteristic",null,null,null,null,null],["Frequency",null,null,null,null,null],[null,"97",null,null,null,null],["Baseline",null,null,null,null,null],["Case",null,null,null,null,null],["Controls",null,null,null,null,null],["\u03b2","SE","N %",null,"Gender",null],["Cases",null,"Sequence","Sequence","Frequency","df"],["Mean","P","Sample","Sample","Species","Characteristic"],["Region","66","86",null,null,null],["Strain",null,null,"12",null,null],["Gene",null,null,null,null,"79"],["\u03b2","24",null,null,null,"30"],["Range",null,"90","55",null,null],["B",null,null,null,"78",null],["N",null,null,null,null,null],["Variable",null,null,null,null,null],["Location","23",null,null,null,null],["Cases",null,null,null,null,null],[null,null,null,"83",null,null],["n","63",null,null,"80",null],["Location",null,"44",null,null,null],[null,"80","93",null,null,null],["Frequency",null,"61",null,null,null],["Species",null,null,null,"82","31"],["p value",null,null,null,null,null],["Male",null,null,null,"27","78"],["Description",null,"96",null,null,null],["Range",null,"56",null,null,null],[null,null,null,null,"91",null],["n %","n %","Description",null,null,null],["Range","Percentage","Baseline",null,null,null],["Group","Name","Type",null,null,null],["Item","95",null,null,null,null],["Women",null,null,null,null,null],["\u03b2",null,null,null,null,null],["Group",null,null,null,null,null],["F","42",null,null,null,null],["N %",null,null,null,null,null],["df",null,null,null,null,null],["N",null,null,null,null,null],["P - value","59",null,null,null,null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{'table_1': '{"columns":["0","1","2"],"index":[0,1,2,3,4,5,6,7,8],"data":[[null,"Specificity","Specificity"],[null,null,null],["Gender",null,null],["Characteristic",null,null],["Frequency",null,null],[null,"97",null],["Baseline",null,null],["Case",null,null],["Controls",null,null]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"data":[["\\u03b2","SE","N %",null,"Gender",null],["Cases",null,"Sequence","Sequence","Frequency","df"],["Mean","P","Sample","Sample","Species","Characteristic"],["Region","66","86",null,null,null],["Strain",null,null,"12",null,null],["Gene",null,null,null,null,"79"],["\\u03b2","24",null,null,null,"30"],["Range",null,"90","55",null,null],["B",null,null,null,"78",null],["N",null,null,null,null,null],["Variable",null,null,null,null,null],["Location","23",null,null,null,null],["Cases",null,null,null,null,null],[null,null,null,"83",null,null],["n","63",null,null,"80",null],["Location",null,"44",null,null,null],[null,"80","93",null,null,null],["Frequency",null,"61",null,null,null],["Species",null,null,null,"82","31"],["p value",null,null,null,null,null],["Male",null,null,null,"27","78"],["Description",null,"96",null,null,null],["Range",null,"56",null,null,null],[null,null,null,null,"91",null]]}', 'table_3': '{"columns":["0","1","2"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["n %","n %","Description"],["Range","Percentage","Baseline"],["Group","Name","Type"],["Item","95",null],["Women",null,null],["\\u03b2",null,null],["Group",null,null],["F","42",null],["N %",null,null],["df",null,null],["N",null,null],["P - value","59",null]]}'}<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "grits"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Wiki Loves Cultura Popular Brasil - Culin\u00e1ria"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": null}, {"Prop0": null, "specific food preparation with cooking finished, and ready to eat, or be served": null}, {"Prop0": null, "specific food preparation with cooking finished, and ready to eat, or be served": "Italian cuisine"}, {"Prop0": "Prop6", "specific food preparation with cooking finished, and ready to eat, or be served": "Polenta"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "m\u0103m\u0103lig\u0103, Ka\u010damak"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"grits"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Wiki Loves Cultura Popular Brasil - Culinária"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Italy"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"cornmeal"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Italian cuisine"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"Polenta"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"mămăligă, Kačamak"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_3: {"columns":["6","7","8"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Age","Genotype","OR 95 % CI"],["Treatment","Outcome","93.21 %"],["P - value","P - value","Variable"],[null,null,null],[null,null,null],[null,null,null],[null,null,null],[null,null,null],[null,"47",null],[null,null,"2"],["73",null,"53"],["45",null,null],[null,null,"89"],[null,null,null],[null,null,"29"],[null,null,null]]} table_piece_4: nan table_piece_5: nan table_piece_2: {"columns":["2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["OR 95 % CI",null,"Gene","Mean \u00b1 SD"],["Gene","Gene",null,"P"],["Gene","Gene","t","P - value"],["55",null,null,null],[null,"0",null,"40"],[null,"27",null,null],["63",null,null,null],["95",null,null,null],[null,null,null,null],[null,null,null,"96"],[null,null,null,"33"],[null,null,null,null],["22",null,null,"53"],[null,null,null,null],[null,null,null,null],[null,null,null,null]]} table_piece_8: nan table_piece_6: nan table_piece_9: nan table_piece_7: nan table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Country","Total"],["p - Value","OR 95 % CI"],["Factor","Strain"],["p - value","18"],["Characteristics",null],[null,null],["Factor",null],["n %",null],["Specificity",null],["Female",null],["B","5"],["Age years",null],["SD","89"],["Variable",null],["Univariate analysis",null],["Species",null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ], "data": [ [ "Country", "Total", "OR 95 % CI", null, "Gene", "Mean ± SD", "Age", "Genotype", "OR 95 % CI" ], [ "p - Value", "OR 95 % CI", "Gene", "Gene", null, "P", "Treatment", "Outcome", "93.21 %" ], [ "Factor", "Strain", "Gene", "Gene", "t", "P - value", "P - value", "P - value", "Variable" ], [ "p - value", "18", "55", null, null, null, null, null, null ], [ "Characteristics", null, null, "0", null, "40", null, null, null ], [ null, null, null, "27", null, null, null, null, null ], [ "Factor", null, "63", null, null, null, null, null, null ], [ "n %", null, "95", null, null, null, null, null, null ], [ "Specificity", null, null, null, null, null, null, "47", null ], [ "Female", null, null, null, null, "96", null, null, "2" ], [ "B", "5", null, null, null, "33", "73", null, "53" ], [ "Age years", null, null, null, null, null, "45", null, null ], [ "SD", "89", "22", null, null, "53", null, null, "89" ], [ "Variable", null, null, null, null, null, null, null, null ], [ "Univariate analysis", null, null, null, null, null, null, null, "29" ], [ "Species", null, null, null, null, null, null, null, null ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "HKNS0", "col1": "Mexico", "col2": "Category:Atole"}, {"col0": "HKNS1", "col1": "Mexico", "col2": "Category:Aguas frescas"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"HKNS0","col1":"Mexico","col2":"Category:Atole"},{"col0":"HKNS1","col1":"Mexico","col2":"Category:Aguas frescas"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_6: nan table_piece_9: nan table_piece_7: nan table_piece_3: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[2,3,4,5,6],"data":[["Gender","SD","F","Univariate analysis","Gene name","P","P","P","Number","B","864.25","Age"],["Variables","97",null,"17",null,"85",null,null,null,null,null,null],["Strain",null,null,null,null,null,null,null,"61",null,null,null],["Gene","46",null,null,null,"95",null,"17","70",null,null,null],["N %",null,null,"16",null,null,null,null,null,null,null,null]]} table_piece_8: nan table_piece_1: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[0],"data":[["Women","Variables","F","Percentage","Percentage","p Value","OR","References",null,"Controls","Variable","Gene"]]} table_piece_5: nan table_piece_4: nan table_piece_2: {"columns":["0","1","2","3","4","5","6","7","8","9","10","11"],"index":[1],"data":[["Genotype","SD","F","Percentage","Percentage","Women","Men","Male","Variables",null,"Characteristic","Item"]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11" ], "index": [ 0, 1, 2, 3, 4, 5, 6 ], "data": [ [ "Women", "Variables", "F", "Percentage", "Percentage", "p Value", "OR", "References", null, "Controls", "Variable", "Gene" ], [ "Genotype", "SD", "F", "Percentage", "Percentage", "Women", "Men", "Male", "Variables", null, "Characteristic", "Item" ], [ "Gender", "SD", "F", "Univariate analysis", "Gene name", "P", "P", "P", "Number", "B", "864.25", "Age" ], [ "Variables", "97", null, "17", null, "85", null, null, null, null, null, null ], [ "Strain", null, null, null, null, null, null, null, "61", null, null, null ], [ "Gene", "46", null, null, null, "95", null, "17", "70", null, null, null ], [ "N %", null, null, "16", null, null, null, null, null, null, null, null ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <|eot_id|><|start_header_id|>user<|end_header_id|>Here is combined table you need to split: {"columns":["0","1","0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],"data":[["Serum sodium","101 mEq \/ L","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features."],["Serum potassium","4 mEq \/ L","Clinicopathological features","No. of patients ( n )","Relative gankyrin expression High","Relative gankyrin expression High","T","P"],["Serum chloride","78 mEq \/ L","Age",null,null,null,"0.785","0.435"],["Serum bicarbonate","23 mEq \/ L","945","9","5 ( 55.6 %)","4 ( 44.4 %)",null,null],["BUN","4 mg \/ dL","$ 45","68","39 ( 57.4 %)","29 ( 42.6 %)",null,null],["Serum creatinine","0.55 mg \/ dL","Gender",null,null,null,"1.898","0.062"],["Random blood glucose","135 mg \/ dL","Male","52","34 ( 65.4 %)","18 ( 34.6 %)",null,null],["Total protein","7.4 g \/ dL","Female","25","11 ( 44.0 %)","14 ( 56.0 %)",null,null],["Serum albumin","3.8 g \/ dL","Lymph node metastasis",null,null,null,"2.401","0.019"],["ALT","90 U \/ L","No.","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["AST","136 U \/ L","Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null],["ALP","153 U \/ L","Distant metastasis",null,null,null,"2.532","0.013"],["Total bilirubin","1.7 mg \/ dL","No","55","27 ( 49.1 %)","28 ( 50.9 %)",null,null],["Direct bilirubin","0.5 mg \/ dL","Yes","22","17 ( 77.3 %)","5 ( 22.7 %)",null,null],["Indirect bilirubin","1.2 mg \/ dL","Pathological grade",null,null,null,"2.557","0.084"],[null,null,"T \u2013 II","9","7 ( 77.8 %)","2 ( 22.2 %)",null,null],[null,null,"III","62","34 ( 54.8 %)","28 ( 45.2 %)",null,null],[null,null,"IV","6","3 ( 50.0 %)","3 ( 50.0 %)",null,null],[null,null,"Vascular invasion",null,null,null,"2.122","0.037"],[null,null,"No","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],[null,null,"Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{'table_1': '{"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],"data":[["Serum sodium","101 mEq \\/ L"],["Serum potassium","4 mEq \\/ L"],["Serum chloride","78 mEq \\/ L"],["Serum bicarbonate","23 mEq \\/ L"],["BUN","4 mg \\/ dL"],["Serum creatinine","0.55 mg \\/ dL"],["Random blood glucose","135 mg \\/ dL"],["Total protein","7.4 g \\/ dL"],["Serum albumin","3.8 g \\/ dL"],["ALT","90 U \\/ L"],["AST","136 U \\/ L"],["ALP","153 U \\/ L"],["Total bilirubin","1.7 mg \\/ dL"],["Direct bilirubin","0.5 mg \\/ dL"],["Indirect bilirubin","1.2 mg \\/ dL"]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],"data":[["TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features.","TAn. t 2 : Association between gankyrin expression and clinicopathological features."],["Clinicopathological features","No. of patients ( n )","Relative gankyrin expression High","Relative gankyrin expression High","T","P"],["Age",null,null,null,"0.785","0.435"],["945","9","5 ( 55.6 %)","4 ( 44.4 %)",null,null],["$ 45","68","39 ( 57.4 %)","29 ( 42.6 %)",null,null],["Gender",null,null,null,"1.898","0.062"],["Male","52","34 ( 65.4 %)","18 ( 34.6 %)",null,null],["Female","25","11 ( 44.0 %)","14 ( 56.0 %)",null,null],["Lymph node metastasis",null,null,null,"2.401","0.019"],["No.","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null],["Distant metastasis",null,null,null,"2.532","0.013"],["No","55","27 ( 49.1 %)","28 ( 50.9 %)",null,null],["Yes","22","17 ( 77.3 %)","5 ( 22.7 %)",null,null],["Pathological grade",null,null,null,"2.557","0.084"],["T \\u2013 II","9","7 ( 77.8 %)","2 ( 22.2 %)",null,null],["III","62","34 ( 54.8 %)","28 ( 45.2 %)",null,null],["IV","6","3 ( 50.0 %)","3 ( 50.0 %)",null,null],["Vascular invasion",null,null,null,"2.122","0.037"],["No","20","9 ( 45.0 %)","11 ( 55.0 %)",null,null],["Yes","57","35 ( 61.4 %)","22 ( 38.6 %)",null,null]]}', 'table_3': nan}<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Tropical Storm Kristy", "col1": "|||.|", "col2": null}, {"col0": "Tropical Storm Yolanda", "col1": "|()|.q", "col2": null}, {"col0": "Tropica1 5torm Ceorgette", "col1": 35.0, "col2": "qqq.()"}, {"col0": "Tropica1 5torm Ceorgette", "col1": 101.9, "col2": 995.0}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Tropical Storm Kristy","col1":111.1,"col2":null},{"col0":"Tropical Storm Yolanda","col1":101.9,"col2":null},{"col0":"Tropical Storm Georgette","col1":35.0,"col2":999.0},{"col0":"Tropical Storm Georgette","col1":101.9,"col2":995.0}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with splitting a combined JSON structure containing two or more tables into separate JSON objects, each representing one table. The input JSON contains a single structure with overlapping or concatenated data from multiple tables. Follow these steps to accurately perform the task: 1. Identify Tables: - The input JSON has overlapping headers, data, and indices. Use logical groupings of columns and indices to determine where one table ends and another begins. 2. Retain Headers, Indices, and Data: - Each extracted table must retain its original `columns`, `index`, and `data` fields. - Ensure that no rows or columns from one table bleed into another. 3. Create Separate JSON Objects: - Create a separate JSON object for each identified table. - Assign a unique key to each table (`table_1`, `table_2`, etc.) for distinction. 4. Ensure Completeness and Accuracy: - Each table must be fully intact, with no missing or misaligned data. - The structure of each JSON object must match the format: ```json { "columns": [...], "index": [...], "data": [...] } ``` 5. Output the Result as a Dictionary: - Combine the separate table JSON objects into a single dictionary with keys corresponding to `table_1`, `table_2`, etc. - Example output format: ```json { "table_1": { "columns": [...], "index": [...], "data": [...] }, "table_2": { "columns": [...], "index": [...], "data": [...] } } ``` 6. Preserve Original Data: - Do not modify the values or formatting of the original data. - Any `null` or special characters should remain as is. 7. Document Any Ambiguities: - If rows or columns overlap ambiguously between tables, document your reasoning for how you assigned them to a specific table. By following these instructions, you will accurately split the input JSON into clearly defined tables without losing any information. <|eot_id|><|start_header_id|>user<|end_header_id|>Here is combined table you need to split: {"columns":["0","1","2","3","4","5"],"index":[0,1,2,0,1,2],"data":[["Txox 2 : Treatment applied to high - energy transyndesmotic ankle fracture dislocation in published reports.","Publication","Syndesmosis fixation ( N \/ total N [% of total N ])","Tibial plafond fixation, N \/ total N (% of total N )","Medial malleolus fixation, N \/ total N (% of total N )",null],["Other treatments applied","Wang et al. [ 1 ]","( 1 ) No syndesmotic screw ( 12 \/ 41 [ 29.39.39.39.39.39.39.39.39.39.39.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.5","Not indicated","27 \/ 41 ( 65.9 %)",null],["( i ) Weight - bearing restrictions ( ii ) Dorsal expansion and plantarflexion starting 2 \u2013 3 weeks posttreatment","Bible et al. [ 2 ]","( i ) One syndesmotic screw ( 4 \/ 23 [ 17.4 %])( ii ) Two syndesmotic screws ( 15 \/ 23 [ 65.2 %])( iii ) Three syndesmotic screws ( 2 \/ 23 [ 8.7 %])","8 \/ 23 ( 34.8 %)","23 \/ 23 ( 100 %)",null],["Tsouz it : Clinical characteristics of high - energy transsyndesmotic ankle fracture dislocation in published reports.","Publication ; type of study","N of patients","Main causes of the injury ( N \/ total N [% of total N ])","Open fracture, N \/ total N (% of total N )","AO - OTA fracture and dislocation classification ( N \/ total N [% of total N ])"],["Associated fractures and injuries ( N \/ total N [% of total N ])","Wang et al. [ 1 ]; retrospective review of patient registry","41","( i ) Traffic accidents ( ii ) [ 125.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.1999","16 \/ 41 ( 39 %)","( i ) 44A ( 3 \/ 41 [ 7.3 %])( ii ) 44B ( 5 \/ 41 [ 12.2 %])( iii ) 44C ( 33 \/ 41 [ 80.5 %])"],["( i ) Distal tibiofilmular ( ii ) Distance ( ii ) Central ( 20.7 %)( ii ) Triangular Igament ( ii ) Triangular Igament ( iii ) Fibula Fracture ( iv ) Medial poollectis ( v ) Medial poollectis ( v ) feacture ( 29 \/ 41 [ 70.7 %)( iii ) injury [ 10 \/ 41 [ 24.4 %]","Bible et al. [ 2 ]; prospective cohort","23","( 1 ) Traffic accidents ( 10 \/ 23 [ 43.5 %])( ii ) Falls from an elevation ( 10 \/ 23 [ 43.5 %])( unspecified ) crush ( 3 \/ 23 [ 13 %])","12 \/ 23 ( 52.2 %)","44B ( 23 \/ 23 [ 100 %])*"]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{'table_1': '{"columns":["0","1","2","3","4"],"index":[0,1,2],"data":[["Txox 2 : Treatment applied to high - energy transyndesmotic ankle fracture dislocation in published reports.","Publication","Syndesmosis fixation ( N \\/ total N [% of total N ])","Tibial plafond fixation, N \\/ total N (% of total N )","Medial malleolus fixation, N \\/ total N (% of total N )"],["Other treatments applied","Wang et al. [ 1 ]","( 1 ) No syndesmotic screw ( 12 \\/ 41 [ 29.39.39.39.39.39.39.39.39.39.39.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.59.5","Not indicated","27 \\/ 41 ( 65.9 %)"],["( i ) Weight - bearing restrictions ( ii ) Dorsal expansion and plantarflexion starting 2 \\u2013 3 weeks posttreatment","Bible et al. [ 2 ]","( i ) One syndesmotic screw ( 4 \\/ 23 [ 17.4 %])( ii ) Two syndesmotic screws ( 15 \\/ 23 [ 65.2 %])( iii ) Three syndesmotic screws ( 2 \\/ 23 [ 8.7 %])","8 \\/ 23 ( 34.8 %)","23 \\/ 23 ( 100 %)"]]}', 'table_2': '{"columns":["0","1","2","3","4","5"],"index":[0,1,2],"data":[["Tsouz it : Clinical characteristics of high - energy transsyndesmotic ankle fracture dislocation in published reports.","Publication ; type of study","N of patients","Main causes of the injury ( N \\/ total N [% of total N ])","Open fracture, N \\/ total N (% of total N )","AO - OTA fracture and dislocation classification ( N \\/ total N [% of total N ])"],["Associated fractures and injuries ( N \\/ total N [% of total N ])","Wang et al. [ 1 ]; retrospective review of patient registry","41","( i ) Traffic accidents ( ii ) [ 125.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.199.1999","16 \\/ 41 ( 39 %)","( i ) 44A ( 3 \\/ 41 [ 7.3 %])( ii ) 44B ( 5 \\/ 41 [ 12.2 %])( iii ) 44C ( 33 \\/ 41 [ 80.5 %])"],["( i ) Distal tibiofilmular ( ii ) Distance ( ii ) Central ( 20.7 %)( ii ) Triangular Igament ( ii ) Triangular Igament ( iii ) Fibula Fracture ( iv ) Medial poollectis ( v ) Medial poollectis ( v ) feacture ( 29 \\/ 41 [ 70.7 %)( iii ) injury [ 10 \\/ 41 [ 24.4 %]","Bible et al. [ 2 ]; prospective cohort","23","( 1 ) Traffic accidents ( 10 \\/ 23 [ 43.5 %])( ii ) Falls from an elevation ( 10 \\/ 23 [ 43.5 %])( unspecified ) crush ( 3 \\/ 23 [ 13 %])","12 \\/ 23 ( 52.2 %)","44B ( 23 \\/ 23 [ 100 %])*"]]}', 'table_3': nan}<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": null, "col1": null}, {"col0": null, "col1": 120}, {"col0": "thorium-209", "col1": 119}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"thorium-211","col1":121},{"col0":"thorium-210","col1":120},{"col0":"thorium-209","col1":119}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_6: nan table_piece_9: nan table_piece_4: nan table_piece_1: {"columns":["0","1","2","3","4"],"index":[0,1,2,3,4],"data":[["Measurements","Standard error","AD","Measurement","Chromosome"],[null,"Expression","Material","LR","Month"],["Poor","Average","Cohort","Cohort",null],["MS","Not specified","Height m","< 0.00001","Body mass index"],["Code","Code","Code","Code","Code"]]} table_piece_7: nan table_piece_5: nan table_piece_8: nan table_piece_2: {"columns":["5","6"],"index":[0,1,2,3,4],"data":[["Parameter","Characteristics"],["Clone","Clone"],["< 0.0001","\u2212 0.2"],["Occupation","\u2212 0.2"],["\u03b2","\u2212 0.2"]]} table_piece_3: nan<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6" ], "index": [ 0, 1, 2, 3, 4 ], "data": [ [ "Measurements", "Standard error", "AD", "Measurement", "Chromosome", "Parameter", "Characteristics" ], [ null, "Expression", "Material", "LR", "Month", "Clone", "Clone" ], [ "Poor", "Average", "Cohort", "Cohort", null, "< 0.0001", "− 0.2" ], [ "MS", "Not specified", "Height m", "< 0.00001", "Body mass index", "Occupation", "− 0.2" ], [ "Code", "Code", "Code", "Code", "Code", "β", "− 0.2" ] ] }, "table_2": null, "table_3": null }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are given several fragments of tables split into different columns. Each column may or may not contain values. Your task is to: 1. Identify Valid Fragments: Select only the fragments that contain data from the given table pieces: `table_piece_1`, `table_piece_2`, `table_piece_3`, `table_piece_4`, `table_piece_5`, `table_piece_6`, `table_piece_7`, `table_piece_8`, `table_piece_9`. 2. Group Fragments by Table: Fragments starting with the same table number (e.g., `table1_`, `table2_`, `table3_`) belong to the same original table. Your job is to merge these fragments back into their respective tables. There may be: - Only `table_1`: If only fragments of `table_1` exist, merge and return only `table_1`. - `table_1` and `table_2`: If fragments of both `table_1` and `table_2` exist, merge each table separately and return both. - All tables (`table_1`, `table_2`, `table_3`): If fragments from all three tables are present, merge each table separately and return all of them. 3. **Merge Fragments**: For each table, combine the valid fragments to create a complete table, maintaining the correct order of columns and rows. 4. **Output the Result in JSON Format**: Present the merged tables as a JSON object. Each table (`table_1`, `table_2`, `table_3`) should be represented separately in the JSON structure as follows: Example: ```json { "table_1": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_2": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] }, "table_3": { "columns": ["Column 1", "Column 2", ...], "index": [row_index_1, row_index_2, ...], "data": [ [row_1_value_1, row_1_value_2, ...], [row_2_value_1, row_2_value_2, ...], ... ] } } Saving Multiple Tables: If multiple tables are present, structure the JSON object with separate keys for table_1, table_2, and table_3. If a table is missing (i.e., no fragments exist for that table), omit it from the output JSON. File Storage: Ensure that the final JSON structure is suitable for saving as a .json file. If only table_1 exists, return only table_1 in the JSON output. If both table_1 and table_2 exist, return both in the JSON output. If all tables (table_1, table_2, table_3) exist, return all of them in the JSON output. Ensure that the final output is correctly structured for saving in JSON format, following the example provided. <|eot_id|><|start_header_id|>user<|end_header_id|>These are the pieces you need to check: table_piece_9: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[8,9,10,11,12,13,14,15,16,17,18],"data":[["Item 15.","932228","214935",null,"31687","801622","801622","801622","384816","338259"],["Market for Registrant \u2019 s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities","932228","450052","633378","17422","307395",null,"616316","856009","593988"],["Amortization","258438","778485","758063","152585","896258","268629","582377","718773","764858"],["001 - 32205","988134","891006","238178","854862","581890","355850","37042","445098","895957"],["Amounts in thousands","965724","868345","104666","486081","683964","52168",null,"979694","686189"],["Amounts in thousands","849220","849220","849220","849220","849220","849220","849220","340411","282216"],["Amounts in thousands","565638","504161","504161","504161","209442",null,"124084","381020","342218"],["Amounts in thousands","211073","211073","174668","47999","200779","408552",null,"852366","363885"],["Amounts in thousands","943642","943642","943642","943642","281284","136662","438459","213770","277976"],["Amounts in thousands","302153","302153","302153",null,"344895","19403","22062","604415","203715"],["Amounts in thousands","302153","302153","302153","843803","215265","737912","687377","530202","236850"]]} table_piece_4: {"columns":["0"],"index":[0,1,2,3,4,5,6,7],"data":[["Insurance"],["Israel"],["Depreciation and amortization"],["Weighted Average Exercise Price"],["Midwest"],["Restructuring charges"],["Restructuring charges"],["Income tax expense benefit"]]} table_piece_8: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[2,3,4,5,6,7],"data":[["TOTAL","243311","277431","215576","825530","754338","432756","432756","432756","432756"],["Financial Services","484958","56801","114994","740092","824369","432756","432756","432756","432756"],["Accounts payable and accrued expenses","597129","71151","630490","630490","630490","630490","630490","630490","281859"],["AmortizedCost","445858","592799","984694","907152","504255","883865","531361","664263",null],["March 31,","932228","531553","534671","564823","345104","898437",null,"888366","805798"],["Total deposits","932228","638855","950321","216356","556570","993264","817752","315888","547653"]]} table_piece_1: {"columns":["0","1"],"index":[0,1,2,3,4,5,6,7,8],"data":[["Female","n %"],["Number","p - value"],["p value","p value"],["Parameter","10"],["Female","40"],["Mean \u00b1 SD",null],["Mean \u00b1 SD",null],["Mean \u00b1 SD",null],["p Value",null]]} table_piece_3: {"columns":["7","8","9","10"],"index":[0,1,2,3,4,5,6,7,8],"data":[[null,null,"Case","Reference"],["Variables","95 % CI","No.","Characteristic"],["Characteristics","Characteristics","Characteristics","Characteristics"],[null,null,null,null],[null,null,null,null],[null,null,null,null],[null,null,"31",null],[null,null,null,null],[null,null,"40",null]]} table_piece_7: {"columns":["0","1","2","3","4","5","6","7","8","9"],"index":[0,1],"data":[["As of December 31,","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt","Loss on extinguishment of debt"],["Item 5.","256712",null,"921765","649620",null,"896325","665176","791054","984568"]]} table_piece_6: {"columns":["2","3","4"],"index":[0,1,2,3,4,5,6,7],"data":[[null,"Net income","Outstanding at end of year"],["5109.40 $","98839.56 $","82214.37 $"],["3084.62 $",null,"99960.66 $"],["11515.59 $",null,"59746.33 $"],["63611.50 $",null,"43386.36 $"],["Restructuring charges","Restructuring charges","Restructuring charges"],["Restructuring charges","Restructuring charges","Restructuring charges"],[null,"51100.42 $","50373.22 $"]]} table_piece_5: {"columns":["1"],"index":[0,1,2,3,4,5,6,7],"data":[["Provision for credit losses"],[null],["48648.38 $"],["99790.31 $"],["84928.75 $"],["Restructuring charges"],["Restructuring charges"],["78.39 $"]]} table_piece_2: {"columns":["2","3","4","5","6"],"index":[0,1,2,3,4,5,6,7,8],"data":[["Frequency","Male","Name","Location","95 % CI"],["Case","p Value","Sensitivity","p Value","Number"],["p value","p value","Characteristics","Characteristics","Characteristics"],[null,null,null,null,null],["79",null,null,"20",null],[null,"70","40",null,null],[null,null,null,null,null],[null,null,null,null,null],[null,null,null,null,null]]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{ "table_1": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ], "data": [ [ "Female", "n %", "Frequency", "Male", "Name", "Location", "95 % CI", null, null, "Case", "Reference" ], [ "Number", "p - value", "Case", "p Value", "Sensitivity", "p Value", "Number", "Variables", "95 % CI", "No.", "Characteristic" ], [ "p value", "p value", "p value", "p value", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics", "Characteristics" ], [ "Parameter", "10", null, null, null, null, null, null, null, null, null ], [ "Female", "40", "79", null, null, "20", null, null, null, null, null ], [ "Mean ± SD", null, null, "70", "40", null, null, null, null, null, null ], [ "Mean ± SD", null, null, null, null, null, null, null, null, "31", null ], [ "Mean ± SD", null, null, null, null, null, null, null, null, null, null ], [ "p Value", null, null, null, null, null, null, null, null, "40", null ] ] }, "table_2": { "columns": [ "0", "1", "2", "3", "4" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "data": [ [ "Insurance", "Provision for credit losses", null, "Net income", "Outstanding at end of year" ], [ "Israel", null, "5109.40 $", "98839.56 $", "82214.37 $" ], [ "Depreciation and amortization", "48648.38 $", "3084.62 $", null, "99960.66 $" ], [ "Weighted Average Exercise Price", "99790.31 $", "11515.59 $", null, "59746.33 $" ], [ "Midwest", "84928.75 $", "63611.50 $", null, "43386.36 $" ], [ "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges" ], [ "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges", "Restructuring charges" ], [ "Income tax expense benefit", "78.39 $", null, "51100.42 $", "50373.22 $" ] ] }, "table_3": { "columns": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 ], "data": [ [ "As of December 31,", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt", "Loss on extinguishment of debt" ], [ "Item 5.", "256712", null, "921765", "649620", null, "896325", "665176", "791054", "984568" ], [ "TOTAL", "243311", "277431", "215576", "825530", "754338", "432756", "432756", "432756", "432756" ], [ "Financial Services", "484958", "56801", "114994", "740092", "824369", "432756", "432756", "432756", "432756" ], [ "Accounts payable and accrued expenses", "597129", "71151", "630490", "630490", "630490", "630490", "630490", "630490", "281859" ], [ "AmortizedCost", "445858", "592799", "984694", "907152", "504255", "883865", "531361", "664263", null ], [ "March 31,", "932228", "531553", "534671", "564823", "345104", "898437", null, "888366", "805798" ], [ "Total deposits", "932228", "638855", "950321", "216356", "556570", "993264", "817752", "315888", "547653" ], [ "Item 15.", "932228", "214935", null, "31687", "801622", "801622", "801622", "384816", "338259" ], [ "Market for Registrant ’ s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities", "932228", "450052", "633378", "17422", "307395", null, "616316", "856009", "593988" ], [ "Amortization", "258438", "778485", "758063", "152585", "896258", "268629", "582377", "718773", "764858" ], [ "001 - 32205", "988134", "891006", "238178", "854862", "581890", "355850", "37042", "445098", "895957" ], [ "Amounts in thousands", "965724", "868345", "104666", "486081", "683964", "52168", null, "979694", "686189" ], [ "Amounts in thousands", "849220", "849220", "849220", "849220", "849220", "849220", "849220", "340411", "282216" ], [ "Amounts in thousands", "565638", "504161", "504161", "504161", "209442", null, "124084", "381020", "342218" ], [ "Amounts in thousands", "211073", "211073", "174668", "47999", "200779", "408552", null, "852366", "363885" ], [ "Amounts in thousands", "943642", "943642", "943642", "943642", "281284", "136662", "438459", "213770", "277976" ], [ "Amounts in thousands", "302153", "302153", "302153", null, "344895", "19403", "22062", "604415", "203715" ], [ "Amounts in thousands", "302153", "302153", "302153", "843803", "215265", "737912", "687377", "530202", "236850" ] ] } }<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Your task is to repair a dataset that has been damaged due to OCR errors. These errors can include missing or altered characters, numbers, or symbols caused by misinterpretation during the OCR process. The goal is to accurately reconstruct the original dataset based on logical patterns, contextual understanding, and any given examples of correctly reconstructed data. Follow these instructions carefully: --- ### Input: You will be provided with a JSON-formatted dataset where: 1. Each row represents an entity with multiple attributes (`col0`, `col1`, `col2`, etc.). 2. Some cells may have incomplete or inaccurate values due to OCR errors (e.g., missing digits, altered text, or symbols). 3. A corrected example dataset might be provided to guide your understanding of expected patterns. --- ### Task: 1. Identify Errors: Examine each cell to detect likely errors, such as: - Missing digits or letters. - Typographical inaccuracies (e.g., "17.0" instead of "167.0"). - Improper text interpretation (e.g., "A Castellana" being interpreted as "A Castellana" but missing critical details). 2. Reconstruct Correct Values: - Use contextual understanding of the data. - Cross-reference similar entries or known patterns in similar datasets. - Refer to provided examples to ensure consistency in reconstruction. 3. Validation: - Ensure numerical values fall within reasonable ranges based on the dataset context (e.g., power station capacities, dam heights, etc.). - Maintain text consistency and proper naming conventions. --- Output Format: Provide the corrected dataset in JSON format, with all errors fixed. Ensure strict adherence to the original structure of the dataset. For example: ```json [ {"col0": "Alcántara dam", "col1": 130.0, "col2": 570}, {"col0": "A Castellana Reservoir", "col1": 7.5, "col2": 20}, {"col0": "Larji hydro power station", "col1": 27.0, "col2": 50}, {"col0": "Koldam hydroelectric power station", "col1": 167.0, "col2": 474}, {"col0": "Nathpa Jhakri Hydro Power station", "col1": 63.0, "col2": 185} ] ``` --- Notes: - Focus on logical reconstruction based on contextual clues and provided examples. - If ambiguity arises and exact reconstruction is not possible, prioritize consistency with similar entries or reference datasets. - Ensure all numerical values are corrected to be logically reasonable (e.g., avoiding incorrect truncation). <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Britvic"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "1998-01-01T00:00:00Z"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Britvic"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"1998-01-01T00:00:00Z"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "paprik\u00e1s"}, {"Prop0": "PropZ", "specific food preparation with cooking finished, and ready to eat, or be served": "Hungary"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Hungarian cuisine"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "meat, Allium, smetana, paprika"}, {"Prop0": "Prop5", "specific food preparation with cooking finished, and ready to eat, or be served": "paprykarz szczeci\u0144ski"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"paprikás"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Hungary"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Hungarian cuisine"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"meat, Allium, smetana, paprika"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"paprykarz szczeciński"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "food, edible in the raw state": "Rhus integrifolia"}, {"Prop0": "Prop2", "food, edible in the raw state": "R3MW7M4V9D"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","food, edible in the raw state":"Rhus integrifolia"},{"Prop0":"Prop2","food, edible in the raw state":"R3MW7M4V9D"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop|", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "PropZ", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "table salt"}, {"Prop0": "Prop3", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"table salt"},{"Prop0":"Prop3","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> Task Objective: Restore missing values in a JSON dataset by filling in appropriate data based on given patterns, logical inferences, and required results. The output should match the expected restoration as provided. Input JSON: The input is a JSON-formatted dataset, where some rows contain missing values (`null`) in one or more columns. Example Input: ```json [ {"col0": "boulevard Chave", "col1": null, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": null, "col1": null, "col2": null}, {"col0": null, "col1": 1185, "col2": 40.0}, {"col0": null, "col1": null, "col2": 37.0}, {"col0": null, "col1": 750, "col2": null}, {"col0": "boulevard Bourdon", "col1": null, "col2": 30.75} ] ``` Expected Output JSON: The output should restore all missing values (`null`) in the dataset, matching the following result: ```json [ {"col0": "boulevard Chave", "col1": 1525, "col2": 25.0}, {"col0": "boulevard Brune", "col1": 1600, "col2": 40.0}, {"col0": "boulevard Carnot", "col1": 570, "col2": 26.0}, {"col0": "boulevard Bessières", "col1": 1185, "col2": 40.0}, {"col0": "boulevard Berthier", "col1": 1750, "col2": 37.0}, {"col0": "Boulevard Beaumarchais", "col1": 750, "col2": 35.0}, {"col0": "boulevard Bourdon", "col1": 585, "col2": 30.75} ] ``` Steps to Solve the Task: 1. Analyze the Dataset: - Understand the patterns and relationships between the columns. - For `col0`, identify names of missing values from the expected output. - For `col1` and `col2`, infer logical numerical relationships or patterns. 2. Restore Missing Values: - Fill missing values in `col0` based on sequential naming patterns or expected values. - Fill missing values in `col1` and `col2` by referencing nearby data and maintaining consistency with the provided output. 3. Validation: - Ensure that the restored dataset matches the given expected output exactly. 4. Output Format: - Return the dataset in a JSON format, preserving the structure of the input. Example Implementation Steps (Pseudo-Code): 1. Load the JSON data. 2. Identify and restore missing values in `col0` based on provided names. 3. For numerical columns (`col1`, `col2`): - Use the average, nearest neighbor values, or specific values as per the example output. 4. Construct the complete dataset and validate it against the expected result. Additional Notes: Ensure the restoration follows the logical patterns and context provided in the example output to avoid arbitrary imputation. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Sjus"}, {"Prop0": null, "kind of liquid which is specifically prepared for human consumption": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Sjus"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"alcohols, soft drink"}]<|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to: 1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields. 2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits. 3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields. 4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation. Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues. ### Input: A JSON string representing a corrupted table dataset. For example: ```json [ {"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0} ] ``` ### Expected Output: A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example: ```json [ {"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014}, {"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0}, {"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0} ] ``` ### Detailed Instructions: 1. Text Corrections: - Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos"). - Validate place names, city names, or any other text-based entries using common naming conventions. 2. Numeric Corrections: - Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`). - Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368"). 3. Pattern Alignment: - Ensure consistency across fields. For example: - `col2` values represent integer IDs or counts. - `col3` values represent float or double-type numbers. 4. Logical Context Validation: - If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds. 5. Output Format: - The corrected JSON should strictly follow the format of the original input while replacing incorrect values. 6. Assumptions: - Assume that the corrected data is logically consistent with the context of each field. - If ambiguities arise, use reasonable guesses based on examples provided or logical patterns. --- Evaluation: The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption. <|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop|", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "PropZ", "type of dish comprised of fish": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<|eot_id|>