<b>Prompting large language models to extrac</b><b>t </b><b>chemical‒disease relation precisely</b><b>and </b><b>comprehensively at </b><b>the </b><b>document level</b>

In this study, we harness the advanced reading comprehension capabilities and extensive world knowledge of LLMs to innovatively construct precise and comprehensive zero-shot prompt workflows for extracting chemical‒disease relationships on the basis of the patterns of LLM-based relation extraction, the attributes of chemical‒disease relationships, and the linguistic features of biomedical literature.The uploaded data includes two compressed files, namely the data and code used in this study. Both the data and code include two folders, the Precise Relation Extraction section and the Comprehensive Relation Extraction section.