  Biomedical triple extraction systems aim to automatically extract biomedical
entities and relations between entities. The exploration of applying large
language models (LLM) to triple extraction is still relatively unexplored. In
this work, we mainly focus on sentence-level biomedical triple extraction.
Furthermore, the absence of a high-quality biomedical triple extraction dataset
impedes the progress in developing robust triple extraction systems. To address
these challenges, initially, we compare the performance of various large
language models. Additionally, we present GIT, an expert-annotated biomedical
triple extraction dataset that covers a wider range of relation types.
