Open-domain Relational Triplet Extraction (ORTE) is the foundation for mining structured knowledge without predefined schemas. Despite the impressive in-context learning capabilities of Large Language Models (LLMs), existing methods are hindered by their reliance on static, heuristic-driven prompting strategies. Due to the lack of reflection mechanisms required to internalize erroneous signals, these methods exhibit vulnerability in semantic ambiguity, often making erroneous extraction patterns permanent. To address this bottleneck, we propose a Knowledge Reconstruction-driven Prompt Optimization (KRPO) framework to assist LLMs in continuously improving their extraction capabilities for complex ORTE task flows. Specifically, we design a self-evaluation mechanism based on knowledge restoration, which provides intrinsic feedback signals by projecting structured triplets into semantic consistency scores. Subsequently, we propose a prompt optimizer based on a textual gradient that can internalize historical experiences to iteratively optimize prompts, which can better guide LLMs to handle subsequent extraction tasks. Furthermore, to alleviate relation redundancy, we design a relation canonicalization memory that collects representative relations and provides semantically distinct schemas for the triplets. Extensive experiments across three datasets show that KRPO significantly outperforms strong baselines in the extraction F1 score.

翻译：开放域关系三元组提取（ORTE）是在无需预定义模式的情况下挖掘结构化知识的基础。尽管大型语言模型（LLMs）在上下文学习方面展现出令人印象深刻的能力，但现有方法受限于其对静态、启发式驱动的提示策略的依赖。由于缺乏内化错误信号所需的反思机制，这些方法在语义模糊性方面表现出脆弱性，常常使错误的提取模式固化。为解决这一瓶颈，我们提出了一个知识重构驱动的提示优化（KRPO）框架，以协助LLMs在复杂的ORTE任务流程中持续提升其提取能力。具体而言，我们设计了一种基于知识重构的自评估机制，通过将结构化三元组映射为语义一致性分数来提供内在的反馈信号。随后，我们提出了一种基于文本梯度的提示优化器，它能够内化历史经验以迭代优化提示，从而更好地指导LLMs处理后续的提取任务。此外，为缓解关系冗余，我们设计了一个关系规范化记忆模块，用于收集代表性关系并为三元组提供语义上不同的模式。在三个数据集上进行的大量实验表明，KRPO在提取F1分数上显著优于现有强基线方法。