The fine-tuned LLaMA-2-7B model is evaluated against the test set from the random 8:1:1 train-validation-test split of USPTO-ORD-100K. Out of the 10K model outputs (completions), only 42 (0.42%) of them are invalid JSON records, and 59 (0.59%) of them are invalid ORD records. Note the former is a sufficient condition for the latter. All of the 42 syntactically JSON invalid completions can be "repaired" by heuristic string operations , such as adding missing quotes or commas, using jsonrepair. After repairing, 9,963 (99.63%) valid ORD records are collected. These results indicate that the fine-tuned model successfully learns the syntax of the ORD's structured data schema during training. Table summarizes the evaluation results at the message level (Evaluation Metric 1). The fine-tuned model is able to extract compound information for ReactionInput entries reliably with an accuracy of 85.58%. Compared with missing compound information in ReactionInput (4.99%, failure mode "Removal"), it is relatively rare (2.26%) for the model to include excess compounds (failure mode "Addition"), and almost all of the excess compounds come from misplacement (e.g., a ProductCompound is placed in ReactionInput) instead of hallucination.