Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor.

Large Language Models (LLMs) have shown impressive capabilities in language understanding and generation, leading to growing interest in zero-shot relation triplet extraction (ZeroRTE), a task that aims to extract triplets for unseen relations without annotated data. However, existing methods typically depend on costly fine-tuning and lack the structured semantic guidance required for accurate and interpretable extraction. To overcome these limitations, we propose FrameRTE, a novel ZeroRTE framework that adopts a “frame first, then extract” paradigm. Rather than extracting triplets directly, FrameRTE first constructs high-quality Relation Semantic Frames (RSFs) through a unified pipeline that integrates frame retrieval, synthesis, and enhancement. These RSFs serve as structured and interpretable knowledge scaffolds that guide frozen LLMs in the extraction process. Building upon these RSFs, we further introduce a human-inspired three-stage reasoning pipeline consisting of semantic frame evocation, frame-guided triplet extraction, and core frame elements validation to achieve semantically constrained extraction. Experiments demonstrate that FrameRTE achieves competitive zero-shot performance on multiple benchmarks. Moreover, the RSFs we construct serve as high-quality semantic resources that can enhance other extraction methods, showcasing the synergy between linguistic knowledge and foundation models.