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However, concerns about reliability, hallucinations, bias, and lack of explainability limit their use in sensitive scenarios. Graph-based representations, such as knowledge graphs and property graphs, offer a promising approach to address these limitations by providing structured knowledge that can validate and explain LLM outputs. The Graph-Enhanced LLMs for Trustworthy Web Data Management (GLOW) workshop explores how graph and Semantic Web technologies can enhance the trustworthiness of LLM-driven data management systems. In particular, it investigates graph-based retrieval and reasoning, ontology-guided modeling, and constraints that support more transparent and accountable LLM-based Web applications. Now in its second edition, GLOW provides a dedicated space for research at the intersection of Semantic Web, Knowledge Graphs, and LLM-driven data-centric AI. 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