The Common Objects in Context (COCO) dataset is a large-scale object detection, segmentation, and captioning dataset. It contains 80 object categories or classes: "person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, NAME_1, hair drier, toothbrush"

Generate a json file with 100 records that contains the class "truck", each record consist of a separate text prompts for stable diffusion that contain this above class, and a list that mention which classes appear in this prompt list. Example:  {"prompt": "A train arriving at the station as a person waits on the platform.", "classes": ["train", "person"]},
Note that you can use synonyms or related term to describe these classes on the prompt. Example {"prompt": "A man standing near a parked car with a bicycle leaning against it.", "classes": ["person", "bicycle"]}
And there is at most 2 classes mention in each prompt.
The content of the file is: 
