# ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool

arXiv: https://arxiv.org/abs/2208.07193 · HTML: https://mlsanity.com/paper/2208.07193/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Context-Dependent Fine-Grained Entity Type Tagging](https://mlsanity.com/paper/1412.1820/) (2014)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)

## Built on by (most cited first, 4 of 4)

- [OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs](https://mlsanity.com/paper/2409.05152/) (2024)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems](https://mlsanity.com/paper/2305.14937/) (2023)
- [LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation](https://mlsanity.com/paper/2605.26956/) (2026)

## Abstract (arXiv)

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs.uni-freiburg.de .