Knowledge Graphs (KGs) serve as a critical foundation for AI systems, yet their automated construction inevitably introduces noise, compromising data trustworthiness. Existing triple verification methods, based on graph embeddings or language models, often suffer from single-source bias by relying on either internal structural constraints or external semantic evidence, and usually follow a static inference paradigm. As a result, they struggle with complex or long-tail facts and provide limited interpretability. To address these limitations, we propose SHARP (Schema-Hybrid Agent for Reliable Prediction), a training-free autonomous agent that reformulates triple verification as a dynamic process of strategic planning, active investigation, and evidential reasoning. Specifically, SHARP combines a Memory-Augmented Mechanism with Schema-Aware Strategic Planning to improve reasoning stability, and employs an enhanced ReAct loop with a Hybrid Knowledge Toolset to dynamically integrate internal KG structure and external textual evidence for cross-verification. Experiments on FB15K-237 and Wikidata5M-Ind show that SHARP significantly outperforms existing state-of-the-art baselines, achieving accuracy gains of 4.2% and 12.9%, respectively. Moreover, SHARP provides transparent, fact-based evidence chains for each judgment, demonstrating strong interpretability and robustness for complex verification tasks.

翻译：知识图谱（KG）是人工智能系统的关键基础，但其自动化构建不可避免引入噪声，损害数据可信度。现有基于图嵌入或语言模型的三元组验证方法往往依赖单一信源偏差——仅利用内部结构约束或外部语义证据，且通常遵循静态推理范式，导致难以处理复杂或长尾事实，可解释性有限。为解决上述局限，我们提出SHARP（模式混合智能体实现可靠预测），一种无需训练的自主智能体，将三元组验证重构为战略规划、主动调查与证据推理的动态过程。具体而言，SHARP将记忆增强机制与模式感知战略规划相结合提升推理稳定性，并采用增强型ReAct循环与混合知识工具集动态整合KG内部结构与外部文本证据进行交叉验证。在FB15K-237和Wikidata5M-Ind上的实验表明，SHARP显著超越现有最优基线，准确率分别提升4.2%和12.9%。此外，SHARP为每个判断提供透明、基于事实的证据链，展现出复杂验证任务中的强可解释性与鲁棒性。