Figure highlights Gemini's outstanding performance in providing comprehensive synthesis details. In our synthesis conditions prompt, we explicitly requested synthesis conditions for every material, which included both MOFs and organic linkers. While GPT-4 and Claude often focused on extracting synthesis conditions for MOFs only, frequently overlooking the synthesis of organic linkers, Gemini consistently searched both the main text and the supporting information to ensure no synthesis detail was omitted. This completeness is reflected in the statistical data, where Gemini received the fewest "\" labels, indicating a more complete and exhaustive extraction process. Gemini's diligence in capturing all relevant synthesis details underscores its potential for tasks that require exhaustive data compilation. These "amazing" responses illustrate that LLMs possess a considerable level of logical reasoning ability when handling complex tasks, showing their potential to significantly improve dataset construction. Their ability to infer information, perform arithmetic operations, and ensure thorough extraction highlights their versatility in dealing with nuanced scientific content. By refining prompts and applying targeted preprocessing to the literature, we believe LLM-based information extraction can become even more accurate and consistent. This progress will make LLMs valuable tools for building comprehensive scientific datasets that support efficient research and discovery.