  This paper presents an experiment conducted during a hackathon, focusing on
using large language models (LLMs) and knowledge graph clustering to extract
entities and relationships from astronomical text. The study demonstrates an
approach to disambiguate entities that can appear in various contexts within
the astronomical domain. By collecting excerpts around specific entities and
leveraging the GPT-4 language model, relevant entities and relationships are
extracted. The extracted information is then used to construct a knowledge
graph, which is clustered using the Leiden algorithm. The resulting Leiden
communities are utilized to identify the percentage of association of unknown
excerpts to each community, thereby enabling disambiguation. The experiment
showcases the potential of combining LLMs and knowledge graph clustering
techniques for information extraction in astronomical research. The results
highlight the effectiveness of the approach in identifying and disambiguating
entities, as well as grouping them into meaningful clusters based on their
relationships.
