E-commerce faces persistent challenges with data quality issue of product listings. Recent advances in Large Language Models (LLMs) offer a promising avenue for automated product listing enrichment. However, LLMs are prone to hallucinations, which we define as the generation of content that is unfaithful to the source input. This poses significant risks in customer-facing applications. Hallucination detection is particularly challenging in the vast e-commerce domain, where billions of products are sold. In this paper, we propose a two-phase approach for detecting hallucinations in LLM-enriched product listings. The first phase prioritizes recall through cost-effective unsupervised techniques. The second phase maximizes precision by leveraging LLMs to validate candidate hallucinations detected in phase one. The first phase significantly reduces the inference space and enables the resource-intensive methods in the second phase to scale effectively. Experiments on two real-world datasets demonstrated that our approach achieved satisfactory recall on unstructured product attributes with suboptimal precision, primarily due to the inherent ambiguity of unstructured attributes and the presence of common sense reasoning. This highlights the necessity for a refined approach to distinguish between common sense and hallucination. On structured attributes with clearly de- fined hallucinations, our approach effectively detected hallucinations with precision and recall surpassing targeted level.