Recently, Large Language Models (LLMs) including GPT-3/3.5/4 and Llama-2 introduce exciting opportunities in materials science for structured information extraction or direct prediction. LLMs can be fine-tuned to output a sequence in desired format, or they also can be used as-is (zero-shot) or with some context or examples given to them (few-shot). Walker et al. published an AuNR recipe dataset using fine-tuned GPT-3 and Llama-2. The structured recipes were extracted directly via joint Named Entity Recognition and Relation Extraction. This work showed that fine-tuning GPT-3 for NERRE task reached 76% accuracy with 240 paper annotations, and comparable performance was reached with Llama-2-13B. Extending from this prior work, we expanded our scope to include all shapes of AuNPs using seed-mediated synthesis. In addition, we manually validated all the recipes, which significantly elevates the quality of the dataset, facilitating further analyses in synthesis science.