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.

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