arXiv:2605.26956v1 Announce Type: cross Abstract: Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -thereby providing a complete end-toend pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustnes
The proliferation of advanced LLMs and their increasing application in practical NLP systems drives the immediate need for robust, generalizable entity linking solutions.
Improved entity linking capabilities, especially with zero-shot domain adaptation, will enhance the accuracy and applicability of AI systems across diverse real-world use cases, making AI agents more effective.
Previously domain-specific and knowledge-base-dependent entity linking is becoming more generalized and adaptable, fostering wider adoption of advanced semantic understanding in AI.
- · AI developers
- · NLP system integrators
- · Companies with diverse data sets
- · AI agents
- · Legacy, domain-specific NLP solutions
- · Manual data annotation services
More accurate and versatile AI-powered data analysis and retrieval systems emerge.
The development of more sophisticated and autonomous AI agents capable of understanding complex, varied textual information accelerates.
Enhanced entity linking contributes to the broader deployment of verifiable AI systems, improving trust in AI outputs across critical applications.
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