The datasets for surrogate model training were generated from the "Chemical reactions from US patents (1976-Sep2016)" dataset 20 by extracting the experimental procedures using a python script. A first dataset was created by rule-based part-of-speech (POS) tagging with ChemicalTagger 4 , followed by exporting the results as xml, and constructing the desired action graph using a rule-based parsing of the POS-tagged xml file. A second dataset was created by annotating the same experimental procedures with the Llama-3.1-8B-Instruct and Llama-3.1-70B-Instruct models 19 using in-context learning (ICL), as well as some manual clean-up instead of the rule-based approach outlined above (see Supporting Information for details). Both datasets were further post-processed to clean up artifacts as explained in the Supporting Information and then used for model training and validation.