Cyber threat intelligence is a critical tool that many organizations and individuals use to protect themselves from sophisticated, organized, persistent, and weaponized cyber attacks. However, few studies have focused on the quality assessment of threat intelligence provided by intelligence platforms, and this work still requires manual analysis by cybersecurity experts. In this paper, we propose a knowledge graph-based verifier, a novel Cyber Threat Intelligence (CTI) quality assessment framework that combines knowledge graphs and Large Language Models (LLMs). Our approach introduces LLMs to automatically extract OSCTI key claims to be verified and utilizes a knowledge graph consisting of paragraphs for fact-checking. This method differs from the traditional way of constructing complex knowledge graphs with entities as nodes. By constructing knowledge graphs with paragraphs as nodes and semantic similarity as edges, it effectively enhances the semantic understanding ability of the model and simplifies labeling requirements. Additionally, to fill the gap in the research field, we created and made public the first dataset for threat intelligence assessment from heterogeneous sources. To the best of our knowledge, this work is the first to create a dataset on threat intelligence reliability verification, providing a reference for future research. Experimental results show that KGV (Knowledge Graph Verifier) significantly improves the performance of LLMs in intelligence quality assessment. Compared with traditional methods, we reduce a large amount of data annotation while the model still exhibits strong reasoning capabilities. Finally, our method can achieve XXX accuracy in network threat assessment.