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A structured Retrieval-Augmented Generation (RAG) framework that leverages annotated mammography reports as in-context demonstrations, enabling joint named entity and relation extraction without task-specific fine-tuning is proposed and achieves performance comparable to fine-tuned BETO models in low-resource settings, while significantly reducing training and deployment overhead.