A new neuro-symbolic framework for constructing knowledge graphs aims to improve the reliability of complex question answering in AI by addressing consistency issues in extracted facts. By implementing a post-extraction correction method, the approach reduces token usage while enhancing the quality of knowledge graphs for symbolic querying.