Overall, the error analysis highlights specific areas where each LLM excelled or struggled. We found that LLMs often lost or misinterpreted numerical information during extraction, such as reagent amounts, temperatures, and concentrations, whereas errors related to reagent or product names were rare. This discrepancy may be due to the inherent mechanisms of LLM generation. Reagent and product names typically appear only once in synthesis descriptions, enabling LLMs to store them accurately without confusion. In contrast, similar numerical data is presented in multiple contexts. This can lead to confusion or overwriting of previously stored information, resulting in cross-contamination of details. Understanding this pattern allows us to better determine which types of information should be prioritized in databases to enhance overall accuracy and reliability.