Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambiguation in a unified framework. Furthermore, our approach leverages large language models to enrich the context of entity mentions, yielding better disambiguation. We evaluated our approach on benchmark datasets and compared with several baselines. The evaluation results show that our approach achieves state-of-the-art performance on out-of-domain datasets.

Entity and relation linking critically impact the accuracy of knowledge graph question answering (KGQA), often limiting the performance of downstream tasks like query generation. While recent advances in large language models (LLMs) offer promising solutions, their role in linking remains underexplored. This work studies how different linking strategies – including both traditional and LLM-based approaches– affect the quality of generated KG queries. We design multiple linking pipelines and use their output to guide structured query construction. Our study not only evaluates linking accuracy, but also the end-to-end impact on query generation. Our experiments show that LLM-based linkers significantly outperform non-LLM methods, particularly in recall. Moreover, we find that high recall—even at the cost of precision—can lead to better overall performance, as LLMs are resilient to input noise. These findings highlight the importance of recall-oriented linking in modern KGQA pipelines.