The power of semantic analysis using LLMs is further underscored by their ability to provide reasoning for their decisions (Figures and). For each of the three criteria, the LLM justifies its answers based on specific paragraphs or sentences in the original literature, leveraging the reasoning capabilities of large language models. To ensure minimal hallucination and sound chemical knowledge, we devised specific prompts tailored to the goal of this study and manually analyzed the reasoning statements (Section S3.3 of the Supporting Information), confirming over 90% correctness in decision-making (Figure ) and in pointing back to the relevant sections of the original manuscripts. Simultaneously, we conducted an ablation study where shorter, less detailed prompts were used. These prompts, lacking strict instructions to reference the original literature and with less specificity on each question, came with more ambiguity in the instruction and resulted in decreased performance of the LLMs, highlighting the importance of detailed and specific prompts for guiding LLMs, particularly in semantic analysis.