{"type":"ScholarlyArticle","title":"LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation","authors":[{"name":"Samy Haffoudhi","slug":"samy-haffoudhi","given":"Samy","family":"Haffoudhi","affiliation":""},{"name":"Nikola Dobričić","slug":"nikola-dobricic","given":"Nikola","family":"Dobričić","affiliation":""},{"name":"Fabian Suchanek","slug":"fabian-suchanek","given":"Fabian","family":"Suchanek","affiliation":""},{"name":"Nils Holzenberger","slug":"nils-holzenberger","given":"Nils","family":"Holzenberger","affiliation":""}],"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-to-end pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings.  In our demo, users can play with the system on their own input texts. All code is publicly available at https://github.com/dig-team/LELA, and a video is at https://www.youtube.com/watch?v=WdupiRjLbR4.","doi":"10.24963/ijcai.2026/966","url":"https://ijcai.org/proceedings/2026/966","pdf":"https://ijcai.org/proceedings/2026/0966.pdf","markdown":"https://ijcai.org/proceedings/2026/966.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/966","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":966,"pages":{"first":8401,"last":8405},"keywords":["AI: Natural Language Processing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}