Regarding Characterization-Free Compliance (Figure ), Gemini again excelled with a compliance rate of 95.7%, surpassing Claude's 84.4% and GPT-4's 80.9%. This indicates that Gemini was most successful in adhering to the requirement of excluding characterization data, which is essential for creating a synthesis conditions-focused dataset. Notably, for the Net-Y-Ratio, which excludes the 12.2% of product data missed by Claude, Claude's performance in both Correctness and Characterization-Free Compliance was comparable to that of Gemini (>96%). This indicates that, when focusing solely on the data that was successfully extracted, both Claude and Gemini generated highly accurate and compliant datasets. The primary difference lay in the scope of coverage, with Claude's dataset ignoring some of the products. Considering that Claude achieved high net ratios (>95%) across all three criteria, we conclude that Claude exhibited the best overall performance in generating synthesis condition databases, balancing both accuracy and coverage.