  Entity Linking (EL) is an essential and challenging task in natural language
processing that seeks to link some text representing an entity within a
document or sentence with its corresponding entry in a dictionary or knowledge
base. Most existing approaches focus on creating elaborate contextual models
that look for clues the words surrounding the entity-text to help solve the
linking problem. Although these fine-tuned language models tend to work, they
can be unwieldy, difficult to train, and do not transfer well to other domains.
Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced
solution to the problems inherent in EL models, but simply naive prompts to
LLMs do not work well. In the present work, we define ChatEL, which is a
three-step framework to prompt LLMs to return accurate results. Overall the
ChatEL framework improves the average F1 performance across 10 datasets by more
than 2%. Finally, a thorough error analysis shows many instances with the
ground truth labels were actually incorrect, and the labels predicted by ChatEL
were actually correct. This indicates that the quantitative results presented
in this paper may be a conservative estimate of the actual performance. All
data and code are available as an open-source package on GitHub at
https://github.com/yifding/In_Context_EL.
