Since 2021, the DarkLab at Lund University (LU) and the Digital Research Service at the French National Institute of Art History (INHA) have collaborated to develop state-of-the-art digital systems and tools for the management and publication of archaeological data, including information from fieldwork and artefact collections. Drawing on the combined expertise of both institutions, this partnership has led to the successful creation and launch of AIR (Archaeological Interactive Report). Following the international workshop on ‘Advanced 3D Archaeological Documentation and Linked Open Data’, held in Lund, Sweden, 17-19 April 2024, we began testing large language models (LLMs) for processing, transforming and interpreting archaeological data, with very promising results. The source data, accessible via the AIR API, is structured in JSON-LD and formalized according to the most widely used ontologies in the field, such as CIDOC CRM and CRM-Archaeo. We have tested two prominent LLMs, GPT-4 by OpenAI and the Mistral Large model by Mistral AI, to analyze this data. In this talk, we will present the results of this experiment: we will focus on data structure, standardized models, and the nuanced challenges of integrating semantics and ontologies into archaeological descriptions and narratives. The presentation will illustrate our approach to improving the interpretation of archaeological data using Large Language Models.