In this work, we used a hybridized approach to exploit the advantages of both searchbased parsers and LLMs. Briefly, we developed a highly precise search-based parser to construct a seed-mediated AuNP dataset from more straightforward recipes to extract and used it to fine-tune an LLM for NERRE task that extracts the structured recipes from more challenging examples. We then manually validated the resulting datasets for further analysis to obtain 492 seed-mediated recipes for AuNPs. We used ML models to generate datadriven hypotheses and verify several known statements on the correlation between synthesis conditions and the morphology and size of synthesized AuNPs using a multi-sourced, textmined dataset from the literature. To our knowledge, this compilation represents the largest manually validated structured database available for AuNPs using seed-mediated growth with a high level of depth and completeness.