We deployed this fine-tuned LLM to extract recipes from hard and partial cases. After running the fine-tuned LLM, the outputs were further post-processed to screen for complete recipes, using the same post-processing algorithm as the clean cases. Also, we applied a hallucination filter checking that every output token is present in the input text, which is consistent with the training data annotation scheme. Then, these datasets were manually validated to check target morphology and recipe (precursors and amounts) were correctly extracted, also categorizing the incorrect extractions as "not in scope", "missing info" or "parser fault". In validating hard and partial recipes, bipyramid, cube, and star syntheses were corrected if possible (in case the recipe is still in scope but the parser output was wrong), and the other morphology recipes were validated but not necessarily corrected. In hard cases, we identified 91 recipes with complete precursor amounts, while for 139 recipes, the emphasis was on accurate precursor categories regardless of amounts (referred to as 'categorical' recipes). As for partial cases, we observed 73 recipes with complete amounts and 124 categorical recipes.