The widespread deployment of large language models (LLMs) across diverse domains has underscored the critical need to ensure the credibility and accuracy of their generated content, particularly in the presence of hallucinations. These hallucinations can severely compromise both the practical performance of models and the security of their applications. In response to this issue, SemEval-2025 Task 3 Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes introduces a more granular task for hallucination detection. This task seeks to identify hallucinations in text, accurately locate hallucinated segments, and assess their credibility. In this paper, we present a three-stage method for fine-grained hallucination detection and localization. First, we transform the text into a triplet representation, facilitating more precise hallucination analysis. Next, we leverage a large language model to generate fact-reference texts that correspond to the triplets. Finally, we employ a fact alignment strategy to identify and localize hallucinated segments by evaluating the semantic consistency between the extracted triplets and the generated reference texts. We evaluate our method on the unlabelled test set across all languages in Task 3, demonstrating strong detection performance and validating its effectiveness in multilingual contexts.