These studies motivate gathering a comprehensive, multi-sourced dataset as a foundation for data-driven studies, rather than one-variable-at-a-time (OVAT) or design of experiments (DoE) approaches. Efforts in the field of solid-state synthesis have demonstrated the value of this approach, with researchers constructing large, structured datasets through text-mining of existing literature on solid-state synthesis, and training machine learning (ML) models for predicting the target stoichiometry, recommending precursor(s) and other synthesis conditions. Cruse et al. published an AuNP synthesis dataset from literature. They utilized a series of machine-learned Named Entity Recognition (NER) to extract entities such as precursor materials, AuNP morphologies, and sizes. Notably, named entities are extracted but they are not connected through relation extraction (RE). To illustrate, if NER detected morphology entities of "nanospheres" and "nanorods" from a paragraph along with the precursor material entities and their amounts, relation extraction would identify which precursors were used for which morphology. Such linking requires further processing, and would ultimately lead to a more complete dataset of structures recipes.