Knowledge Graphs (KGs) play a critical role in structured knowledge representation and downstream applications such as semantic search and intelligent decision-making. However, applying Large Language Models (LLMs) to KG construction still faces challenges, including the generation of hallucinated facts that undermine reliability and the requirement of costly fine-tuning to adapt to specialized domains. To address these issues, we propose TFreeKGGen, a training-free model for end-to-end KG generation. The model leverages a hierarchical prompt design to fully exploit the semantic understanding of LLMs, and introduces a two-stage verification–correction mechanism that grounds extracted triples in source evidence and rectifies hallucinated outputs. Experiments on datasets from different domains demonstrate consistent improvement gained by TFreeKGGen. Moreover, we construct the first coalbed methane safety KG, demonstrating the model’s effectiveness in knowledge modeling. These results establish TFreeKGGen as an efficient and trustworthy pathway for scalable KG construction without domain-specific training, contributing to the advancement of knowledge engineering and intelligent decision support.