Biomedical relation extraction (BioRE) supports knowledge graph construction and hypothesis generation.
In real-world settings, most candidate entity pairs are unrelated.
Symbolic systems such as SemRep offer high precision and interpretability but limited recall, whereas large language models (LLMs) can produce false-positive relations.
We developed ANCHOR-RE, an agentic framework that combines retrieved demonstrations, ontology-guided semantic constraints, external biomedical knowledge, and error-driven verification to improve BioRE reliability without task-specific fine-tuning.
We evaluated ANCHOR-RE on SemRepGS, DDI, and ChemProt using proprietary and open-weight LLMs.
To assess generalizability while reducing potential pretraining contamination, we also conducted a post-cutoff temporal precision evaluation using 100 biomedical articles published in 2026.
With the proprietary backbone, ANCHOR-RE improved micro-F1 over direct prompting from 0.654 to 0.676 on SemRepGS, from 0.769 to 0.881 on DDI, and from 0.937 to 0.941 on ChemProt, outperforming previously reported inference-only methods on DDI and ChemProt.
Gains were also observed with the open-weight backbone.
Additional experiments demonstrated cross-family model compatibility and sensitivity to external knowledge quality.
Manual assessment of 500 randomly sampled positive predictions from the post-cutoff set yielded 69% precision.
These results show that contextual evidence and error-driven verification can improve BioRE reliability across benchmarks, model families, and newly published literature, supporting ANCHOR-RE as a practical training-free approach to biomedical literature mining.