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<|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": "Chalco", "col1": null, "col2": "Velk\u00e1 pardubick\u00e1 2002"}, {"col0": "Clyde Van Dusen", "col1": "Clyde Van Dusen", "col2": null}, {"col0": "George Washington", "col1": "George Washington", "col2": null}, {"col0": "Dahlia", "col1": "Dahlia", "col2": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Chalco","col1":null,"col2":"Velká pardubická 2002"},{"col0":"Clyde Van Dusen","col1":"Clyde Van Dusen","col2":null},{"col0":"George Washington","col1":"George Washington","col2":null},{"col0":"Dahlia","col1":"Dahlia","col2":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.
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0 : mObj1.hashCode()) + (mObj2 == null ? 0 : mObj2.hashCode())==0","col5":"_"},{"Unnamed: 0":3,"col0":"org.apache.commons.lang.time.FastDateFormat#?#FastDateFormat#?,String,TimeZone,Locale#551","col1":0.0,"col2":0.2269439697,"col3":0.1361663818,"col4":"!(locale == null)","col5":"_"},{"Unnamed: 0":4,"col0":"org.apache.commons.lang.time.FastDateFormat#String#parseToken#?,String,int[]#740","col1":0.0,"col2":0.1730769231,"col3":0.1038461538,"col4":"c == '\\''","col5":"_"},{"Unnamed: 0":5,"col0":"org.apache.commons.lang.time.FastDateFormat#void#init#?#566","col1":0.0,"col2":0.0220675216,"col3":0.013240513,"col4":"mRules[i].estimateLength()==i","col5":"_"},{"Unnamed: 0":6,"col0":"org.apache.commons.lang.time.FastDateFormat#int#hashCode#?#1007","col1":0.0,"col2":0.0220675216,"col3":0.013240513,"col4":"total>=0","col5":"_"},{"Unnamed: 0":7,"col0":"org.apache.commons.lang.time.FastDateFormat#FastDateFormat#getInstance#?,String,TimeZone,Locale#214","col1":0.0,"col2":0.0104914769,"col3":0.0062948862,"col4":"format == null","col5":"_"},{"Unnamed: 0":8,"col0":"org.apache.commons.lang.time.FastDateFormat$TimeZoneNameRule#?#TimeZoneNameRule#?,TimeZone,boolean,Locale,int#1544","col1":0.0,"col2":0.0,"col3":0.0,"col4":"!(timeZoneForced)","col5":"_"},{"Unnamed: 0":9,"col0":"org.apache.commons.lang.time.FastDateFormat$CharacterLiteral#int#estimateLength#?#1089","col1":0.0,"col2":0.0,"col3":0.0,"col4":"1>0","col5":"_"},{"Unnamed: 0":10,"col0":"org.apache.commons.lang.time.FastDateFormat$TextField#int#estimateLength#?#1161","col1":0.0,"col2":0.0,"col3":0.0,"col4":"max>0","col5":"_"},{"Unnamed: 0":11,"col0":"org.apache.commons.lang.time.FastDateFormat$PaddedNumberField#?#PaddedNumberField#?,int,int#1273","col1":0.0,"col2":0.0,"col3":0.0,"col4":"!(size < 3)","col5":"_"},{"Unnamed: 0":12,"col0":"org.apache.commons.lang.time.FastDateFormat$Pair#boolean#equals#?,Object#1717","col1":0.0,"col2":0.0,"col3":0.0,"col4":"!(this == obj)","col5":"_"},{"Unnamed: 0":13,"col0":"org.apache.commons.lang.time.FastDateFormat#FastDateFormat#getDateTimeInstance#?,int,int,TimeZone,Locale#462","col1":0.0,"col2":0.0,"col3":0.0,"col4":"!(timeZone != null)","col5":"GT"},{"Unnamed: 0":14,"col0":"org.apache.commons.lang.time.FastDateFormat$TwelveHourField#int#estimateLength#?#1455","col1":0.0,"col2":0.0,"col3":0.0,"col4":"mRule.estimateLength()>=0","col5":"_"},{"Unnamed: 0":15,"col0":"org.apache.commons.lang.time.FastDateFormat$TwoDigitNumberField#int#estimateLength#?#1340","col1":0.0,"col2":0.0,"col3":0.0,"col4":"2>=0","col5":"_"},{"Unnamed: 0":16,"col0":"org.apache.commons.lang.time.FastDateFormat$PaddedNumberField#int#estimateLength#?#1285","col1":0.0,"col2":0.0,"col3":0.0,"col4":"4>=0","col5":"_"},{"Unnamed: 0":17,"col0":"org.apache.commons.lang.time.FastDateFormat$StringLiteral#int#estimateLength#?#1120","col1":0.0,"col2":0.0,"col3":0.0,"col4":"mValue.length()>0","col5":"_"},{"Unnamed: 0":18,"col0":"org.apache.commons.lang.time.FastDateFormat$UnpaddedNumberField#int#estimateLength#?#1193","col1":0.0,"col2":0.0,"col3":0.0,"col4":"4!=0","col5":"_"},{"Unnamed: 0":19,"col0":"org.apache.commons.lang.time.FastDateFormat$TimeZoneNameRule#int#estimateLength#?#1560","col1":0.0,"col2":0.0,"col3":0.0,"col4":"4>=0","col5":"_"}]<|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": "Turtle Pendant", "col1": 5.08, "col2": 6.67}, {"col0": "Turtle Pendant", "col1": 2.22, "col2": 2.86}, {"col0": "Turtle Pendant", "col1": 6.28, "col2": 7.43}, {"col0": "Armadillo Pendant", "col1": 2.54, "col2": 4.13}, {"col0": "Monkey Pendant", "col1": 2.54, "col2": "G.3S"}, {"col0": "Monkey Pendant", "col1": 2.86, "col2": 6.03}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Turtle Pendant","col1":5.08,"col2":6.67},{"col0":"Turtle Pendant","col1":2.22,"col2":2.86},{"col0":"Turtle Pendant","col1":6.28,"col2":7.43},{"col0":"Armadillo Pendant","col1":2.54,"col2":4.13},{"col0":"Monkey Pendant","col1":2.54,"col2":6.35},{"col0":"Monkey Pendant","col1":2.86,"col2":6.03}]<|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", "kind of liquid which is specifically prepared for human consumption": "http://kalimarkbovonto.com"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "India"}, {"Prop0": "Prop3", "kind of liquid which is specifically prepared for human consumption": "1916-01-01T00:00:00Z"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"http:\/\/kalimarkbovonto.com"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"India"},{"Prop0":"Prop3","kind of liquid which is specifically prepared for human consumption":"1916-01-01T00:00:00Z"}]<|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", "type of food based on potatoes": "Switzerland, Austria"}, {"Prop0": "Prop2", "type of food based on potatoes": "butter, flour, potato"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of food based on potatoes":"Switzerland, Austria"},{"Prop0":"Prop2","type of food based on potatoes":"butter, flour, potato"}]<|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", "ingredient used in food product": "spined loach"}, {"Prop0": "Prop2", "ingredient used in food product": "spined loach"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","ingredient used in food product":"spined loach"},{"Prop0":"Prop2","ingredient used in food product":"spined loach"}]<|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": "Wannefeld", "col1": null}, {"col0": "Niederleierndorf", "col1": 97.0}, {"col0": "Winden (Kreuzau)", "col1": null}, {"col0": "M\u00fcggenhausen", "col1": null}, {"col0": "Reupzig", "col1": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Wannefeld","col1":null},{"col0":"Niederleierndorf","col1":97.0},{"col0":"Winden (Kreuzau)","col1":null},{"col0":"Müggenhausen","col1":null},{"col0":"Reupzig","col1":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": "Frenchman Butte", "col1": "national historic site of Canada", "col2": "schist", "col3": null}, {"col0": "Brandberg", "col1": "national monument of Namibia", "col2": "granite", "col3": null}, {"col0": "Aavasaksa", "col1": "part of UNESCO World Heritage Site", "col2": "pluton", "col3": null}, {"col0": "Katahdin", "col1": "National Natural Landmark", "col2": "granite", "col3": null}, {"col0": "Arrigetch Peaks", "col1": "National Natural Landmark", "col2": null, "col3": "IUCN category III: Natural Monument or Feature"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Frenchman Butte","col1":"national historic site of Canada","col2":"schist","col3":null},{"col0":"Brandberg","col1":"national monument of Namibia","col2":"granite","col3":null},{"col0":"Aavasaksa","col1":"part of UNESCO World Heritage Site","col2":"pluton","col3":null},{"col0":"Katahdin","col1":"National Natural Landmark","col2":"granite","col3":null},{"col0":"Arrigetch Peaks","col1":"National Natural Landmark","col2":null,"col3":"IUCN category III: Natural Monument or Feature"}]<|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": "Copper Cove", "col1": "Antarctic Treaty area"}, {"col0": "Warren Cove", "col1": "Massachusetts"}, {"col0": "Kennedy Cove", "col1": "Antarctic Treaty area"}, {"col0": "C1en GoVe", "col1": "Pierce County"}, {"col0": "Glen Cove", "col1": "Jefferson County"}, {"col0": "Sealers Cove", "col1": "Victoria"}, {"col0": "Deep Cove", "col1": "Southland District"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Copper Cove","col1":"Antarctic Treaty area"},{"col0":"Warren Cove","col1":"Massachusetts"},{"col0":"Kennedy Cove","col1":"Antarctic Treaty area"},{"col0":"Glen Cove","col1":"Pierce County"},{"col0":"Glen Cove","col1":"Jefferson County"},{"col0":"Sealers Cove","col1":"Victoria"},{"col0":"Deep Cove","col1":"Southland District"}]<|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": "Russian cuisine, Polish cuisine"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "http://commons.wikimedia.org/wiki/Special:FilePath/Ru-%D0%BA%D0%B8%D1%81%D0%B5%D0%BB%D1%8C.ogg"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Kissel"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Russian cuisine, Polish cuisine"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"http:\/\/commons.wikimedia.org\/wiki\/Special:FilePath\/Ru-%D0%BA%D0%B8%D1%81%D0%B5%D0%BB%D1%8C.ogg"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Kissel"}]<|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", "kind of liquid which is specifically prepared for human consumption": "Gunpowder tea"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "People's Republic of China"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Gunpowder tea"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"People's Republic of China"}]<|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.
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burgers","col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":null,"col14":null},{"col0":"IUWU35","col1":"meat","col2":"BK Stacker","col3":"United States of America","col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":null,"col14":null},{"col0":"IUWU36","col1":null,"col2":null,"col3":"Japan","col4":null,"col5":"2009-01-30T00:00:00Z","col6":"Yokosuka","col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":"Yokosuka","col14":"Japan"},{"col0":"IUWU37","col1":null,"col2":"McGriddle","col3":null,"col4":"McDonald’s","col5":"2003-01-01T00:00:00Z","col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":null,"col14":null},{"col0":"IUWU38","col1":"bread roll, pork meat, chicken meat, horse meat","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"cuisine of Belgium","col12":null,"col13":null,"col14":null},{"col0":"IUWU39","col1":"pilaf","col2":"Loco moco","col3":"United States of America","col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":null,"col14":null},{"col0":"IUWU40","col1":null,"col2":null,"col3":"United States of America","col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":null,"col12":null,"col13":null,"col14":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": "Line 2", "col1": "Chongqing Rail Transit (Group) Co., Ltd.", "col2": "2005-06-18"}, {"col0": "Line 1", "col1": "Scomi", "col2": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Line 2","col1":"Chongqing Rail Transit (Group) Co., Ltd.","col2":"2005-06-18"},{"col0":"Line 1","col1":"Scomi","col2":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": "Hargpies", "col1": 42.49344}, {"col0": "Dvor", "col1": 502.3755}, {"col0": "Rostbjnica", "col1": 8512.0}, {"col0": "Jian", "col1": 3317.74209}, {"col0": "Xapur", "col1": 5250.931}, {"col0": "Ipidles", "col1": 1708.707}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Hargpies","col1":42.49344},{"col0":"Dvor","col1":502.3755},{"col0":"Kostbjnica","col1":8512.0},{"col0":"Jian","col1":3317.74209},{"col0":"Xapur","col1":5250.931},{"col0":"Ipidles","col1":1708.707}]<|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": "FWOL0", "col1": "Allium", "col2": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=2413", "col3": "edible plant or part of a plant, involved in cooking"}, {"col0": "FWOL1", "col1": "Brassica oleracea", "col2": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=319629", "col3": "?"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"FWOL0","col1":"Allium","col2":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=2413","col3":"edible plant or part of a plant, involved in cooking"},{"col0":"FWOL1","col1":"Brassica oleracea","col2":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=319629","col3":"?"}]<|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": "PEXI0", "col1": "Croton megalocarpus", "col2": null, "col3": null, "col4": "?"}, {"col0": "PEXI1", "col1": "Croton cascarilloides", "col2": "8007-06-5", "col3": "N81EA2A6NP", "col4": "?"}, {"col0": "PEXI2", "col1": "Croton tiglium", "col2": "8001-28-3", "col3": "WK97EQG57S", "col4": "any of a varity of oils derived from plants of genus Croton"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"PEXI0","col1":"Croton megalocarpus","col2":null,"col3":null,"col4":"?"},{"col0":"PEXI1","col1":"Croton cascarilloides","col2":"8007-06-5","col3":"N81EA2A6NP","col4":"?"},{"col0":"PEXI2","col1":"Croton tiglium","col2":"8001-28-3","col3":"WK97EQG57S","col4":"any of a varity of oils derived from plants of genus Croton"}]<|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": "Reinfelden-Nollingen", "col1": "Germany"}, {"col0": "mfilochia Munjcipal Unit", "col1": "Greece"}, {"col0": "Gorgopotamos Municipa Unit", "col1": "Greece"}, {"col0": "DreWtz", "col1": "Germany"}, {"col0": "hst\u00e4dt", "col1": "Germany"}, {"col0": "Qppem", "col1": "Belgium"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Reinfelden-Nollingen","col1":"Germany"},{"col0":"mfilochia Munjcipal Unit","col1":"Greece"},{"col0":"Gorgopotamos Municipa Unit","col1":"Greece"},{"col0":"Drewtz","col1":"Germany"},{"col0":"hstädt","col1":"Germany"},{"col0":"Qppem","col1":"Belgium"}]<|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": "Arviat A", "col1": 10.4}, {"col0": "Arviat A", "col1": 10.4}, {"col0": "Ekati A", "col1": 468.2}, {"col0": "Lutselk'E A", "col1": 178.6}, {"col0": "Yellowknife A", "col1": 205.7}, {"col0": "4e11oWknife A", "col1": 205.7}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Arviat A","col1":10.4},{"col0":"Arviat A","col1":10.4},{"col0":"Ekati A","col1":468.2},{"col0":"Lutselk'E A","col1":178.6},{"col0":"Yellowknife A","col1":205.7},{"col0":"Yellowknife A","col1":205.7}]<|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": "Met Museum artworks on display"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Metropolitan Museum of Art, Asian Art"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Met Museum artworks on display"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Metropolitan Museum of Art, Asian Art"}]<|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": "Sulz", "col1": 3.02, "col2": null, "col3": 2527.0}, {"col0": "Irdning", "col1": 21.95, "col2": 205.0, "col3": 2749.0}, {"col0": "Neuaigen", "col1": 11.3423, "col2": null, "col3": 377.0}, {"col0": "Motten", "col1": "3.4q", "col2": 72.0, "col3": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Sulz","col1":3.02,"col2":null,"col3":2527.0},{"col0":"Irdning","col1":21.95,"col2":205.0,"col3":2749.0},{"col0":"Neuaigen","col1":11.3423,"col2":null,"col3":377.0},{"col0":"Motten","col1":3.49,"col2":72.0,"col3":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": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Goulash soup"}, {"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": "soup"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "beef, paprika, onion"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Goulash soup"},{"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":"soup"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"beef, paprika, onion"}]<|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": "YMKS0", "col1": "carrot, rice, meat, Allium cepa, vegetable oil", "col2": "Category:Pilaf", "col3": null, "col4": null, "col5": null, "col6": "Brockhaus and Efron Encyclopedic Dictionary, Small Brockhaus and Efron Encyclopedic Dictionary", "col7": null, "col8": null, "col9": null, "col10": 6499443011.0, "col11": "dish made from rice"}, {"col0": "YMKS1", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS2", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS3", "col1": "rice, chicken meat", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS4", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS5", "col1": null, "col2": "Category:Pakistani rice dishes", "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS6", "col1": "herb", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS7", "col1": "rice", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS8", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "4MR5q", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS10", "col1": "rice, European anchovy", "col2": null, "col3": "Turkish cuisine", "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS11", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": "Wiki Loves Cultura Popular Brasil - Culin\u00e1ria", "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS12", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS13", "col1": "rice", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS14", "col1": null, "col2": "Gategory:Gooked rice", "col3": "sushi", "col4": null, "col5": null, "col6": "Gujin Tushu Jicheng", "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS15", "col1": "rice, beef, goat meat, fish as food, chicken meat, shrimp or prawn", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS16", "col1": "rice, beef", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS17", "col1": "rice", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS18", "col1": "g\u1ea5c", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS19", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "VMR5Z()", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS21", "col1": "Cocos nucifera, Oryza, rice flour", "col2": null, "col3": "cuisine of Kerala", "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS22", "col1": null, "col2": null, "col3": null, "col4": "Fast JaVa, Cresik", "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS23", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS24", "col1": "rice, egg", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS25", "col1": null, "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS26", "col1": "g1utinous rice", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "?"}, {"col0": "YMKS27", "col1": "pea, risotto rice", "col2": null, "col3": null, "col4": null, "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS28", "col1": null, "col2": null, "col3": null, "col4": "West Java", "col5": null, "col6": null, "col7": null, "col8": null, "col9": null, "col10": null, "col11": "dish made from rice"}, {"col0": "YMKS29", "col1": null, "col2": null, 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rice"},{"col0":"YMKS109","col1":null,"col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"?"},{"col0":"YMKS110","col1":null,"col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS111","col1":"ropa vieja, frijoles negros, tajada, white rice","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS112","col1":null,"col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS113","col1":null,"col2":null,"col3":null,"col4":null,"col5":"Kasari","col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS114","col1":"rice, table salt, oil, Cicer arietinum, beef, tomato 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rice"},{"col0":"YMKS120","col1":null,"col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"?"},{"col0":"YMKS121","col1":"rice","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":"Wiki Loves Cultura Popular Brasil - Culinária","col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS122","col1":"turkey meat","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS123","col1":"rice","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"dish made from rice"},{"col0":"YMKS124","col1":null,"col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"?"},{"col0":"YMKS125","col1":"chicken meat","col2":null,"col3":null,"col4":null,"col5":null,"col6":null,"col7":null,"col8":null,"col9":null,"col10":null,"col11":"?"}]<|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": "Bglizg", "col1": null}, {"col0": "Lordan", "col1": null}, {"col0": "Republic of the ongo", "col1": null}, {"col0": "Guatemcla", "col1": null}, {"col0": "ermany", "col1": 2.3161}, {"col0": "Sria", "col1": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Bglizg","col1":null},{"col0":"Lordan","col1":null},{"col0":"Republic of the ongo","col1":null},{"col0":"Guatemcla","col1":null},{"col0":"ermany","col1":2.3161},{"col0":"Sria","col1":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": "Prop1", "cheese from France": "appellation d'origine contr\u00f4l\u00e9e, protected designation of origin"}, {"Prop0": "Prop2", "cheese from France": "3S()()()()()"}, {"Prop0": "Prop3", "cheese from France": "France"}, {"Prop0": "Prop4", "cheese from France": "http://www.pont-leveque-aoc.org/"}, {"Prop0": "Prop5", "cheese from France": "Entity[\"FoodType\", \"PontLEveque\"]"}, {"Prop0": "Prop6", "cheese from France": "France"}, {"Prop0": "Prop7", "cheese from France": "Z33Z"}, {"Prop0": "Prop8", "cheese from France": "cow's milk"}, {"Prop0": "Prop9", "cheese from France": "Calvados, Eure, Manche, Orne, Seine-Maritime"}, {"Prop0": "Prop10", "cheese from France": "Pont-l'\u00e9v\u00eaque (cheese)"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","cheese from France":"appellation d'origine contrôlée, protected designation of origin"},{"Prop0":"Prop2","cheese from France":"3500000"},{"Prop0":"Prop3","cheese from France":"France"},{"Prop0":"Prop4","cheese from France":"http:\/\/www.pont-leveque-aoc.org\/"},{"Prop0":"Prop5","cheese from France":"Entity[\"FoodType\", \"PontLEveque\"]"},{"Prop0":"Prop6","cheese from France":"France"},{"Prop0":"Prop7","cheese from France":"2332"},{"Prop0":"Prop8","cheese from France":"cow's milk"},{"Prop0":"Prop9","cheese from France":"Calvados, Eure, Manche, Orne, Seine-Maritime"},{"Prop0":"Prop10","cheese from France":"Pont-l'évêque (cheese)"}]<|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": "Guys", "col1": "1997-01-01"}, {"col0": "Signal to Noise", "col1": "1992-01-01"}, {"col0": "Level Up", "col1": "2011-01-01"}, {"col0": "Grandville", "col1": "2009-01-01"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Guys","col1":"1997-01-01"},{"col0":"Signal to Noise","col1":"1992-01-01"},{"col0":"Level Up","col1":"2011-01-01"},{"col0":"Grandville","col1":"2009-01-01"}]<|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": "Emirates Towers", "col1": 17, "col2": "high-tech architecture"}, {"col0": "Scotia Place", "col1": 8, "col2": "modern architecture"}, {"col0": "Emerald Towers", "col1": 18, "col2": "modern architecture"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Emirates Towers","col1":17,"col2":"high-tech architecture"},{"col0":"Scotia Place","col1":8,"col2":"modern architecture"},{"col0":"Emerald Towers","col1":18,"col2":"modern architecture"}]<|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": "Die Lupe", "col1": "Brunswick"}, {"col0": "Katona Jozsef Theatre in Budapest", "col1": "Budapest District V"}, {"col0": "Chicago Theatre", "col1": "Chicago"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Die Lupe","col1":"Brunswick"},{"col0":"Katona Jozsef Theatre in Budapest","col1":"Budapest District V"},{"col0":"Chicago Theatre","col1":"Chicago"}]<|eot_id|> |
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