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End-to-end entity linking presents a promising solution by disambiguating  entity mentions and aligning them with knowledge bases. However, most existing approaches are tailored to a  single KB, limiting their adaptability and scalability across diverse knowledge resources. To address this limitation,  we introduce FELA - Flexible Entity Linking Approach - a framework designed for seamless entity linking across  multiple knowledge bases. FELA leverages fine-tuned Large Language Models, a generic embedding model, and a  Large Language Model-based reranking module to enhance entity disambiguation. Our approach achieves state-  of-the-art performance on Wikidata entity linking benchmarks, demonstrating its effectiveness and flexibility.  Furthermore, we illustrate FELA’s extensibility by applying it to Agrovoc, showcasing its capability to generalize  beyond Wikidata. 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