As an alternate approach and point of comparison, we explored Table Evaluation results at the message level (Evaluation Metric 1) for structured records extracted using the fine-tuned LLaMA-2-7B model. The "Path" column denotes the root path of the corresponding messages in a Reaction message. * These values were calculated using a more lenient routine detailed in the main text. extracting structured data with pretrained LLMs directly using the chain-of-thought prompting method, 52 a few-shot training method by engineering the prompts such that they mimic the thought processes of a human when solving a complicated task. This method is easier to deploy compared to the fine-tuning methods; however, it could only produce syntactically correct ORD data in 408 out of 500 cases after repairing with accuracies of 61.19% and 31.28% for Compound and ProductCompound, respectively, indicating that chain-of-thought prompting without finetuning is likely insufficient for this task. This prompting method is also limited by human-crafted instructions and the context window of the model, and, considering there are more than 600 different fields defined in ORD schema, preparing examples and steps to extract a full Reaction record seems impractical. Details of our implementation and evaluation can be found in Supporting Information.