# LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation

arXiv: https://arxiv.org/abs/2605.26956 · HTML: https://mlsanity.com/paper/2605.26956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 13 of 13)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks](https://mlsanity.com/paper/2406.12925/) (2024)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder](https://mlsanity.com/paper/2602.18487/) (2026)
- [ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool](https://mlsanity.com/paper/2208.07193/) (2022)
- [Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2307.13497/) (2023)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)

## Built on by (most cited first, 1 of 1)

- [GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base](https://mlsanity.com/paper/2608.06992/) (2026)

## Abstract (arXiv)

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 robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts.