This paper presents several strategies to automatically obtain additional examples for incontext learning, effectively transforming relation extraction from a 1-shot to a few-shot setting.Specifically, we introduce a novel strategy for example selection, in which new examples are selected based on the similarity of their underlying syntactic-semantic structure to the provided 1-shot example.We show that our strategy results in complementary word choices and sentence structures compared to LLM-generated examples.When both strategies are combined, the resulting hybrid system achieves a more holistic picture of the relations of interest than either method alone.Our framework transfers well across datasets (FS-TACRED and FS-FewRel) and LLM families (Qwen and Gemma).Overall, our hybrid system consistently outperforms alternative strategies achieving state-of-the-art performance on FS-TACRED and strong gains on a customized FewRel subset.