From Show Programmes to Data: Designing a Workflow to Make Performing Arts Ephemera Accessible Through Language Models

• URL: http://arxiv.org/abs/2512.07452v1
• Date: Mon, 08 Dec 2025 11:27:10 GMT
• Title: From Show Programmes to Data: Designing a Workflow to Make Performing Arts Ephemera Accessible Through Language Models
• Authors: Clarisse Bardiot, Pierre-Carl Langlais, Bernard Jacquemin, Jacob Hart, Antonios Lagarias, Nicolas Foucault, Aurélie Lemaître-Legargeant, Jeanne Fras,
• Abstract summary: We show how vision-language models can accurately parse and transcribe born-digital and digitised programmes.<n>We train a reasoning model (POntAvignon) using reinforcement learning with both formal and semantic rewards.<n>This approach enables automated RDF triple generation and supports alignment with existing knowledge graphs.
• Score: 0.3331620034375478
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Many heritage institutions hold extensive collections of theatre programmes, which remain largely underused due to their complex layouts and lack of structured metadata. In this paper, we present a workflow for transforming such documents into structured data using a combination of multimodal large language models (LLMs), an ontology-based reasoning model, and a custom extension of the Linked Art framework. We show how vision-language models can accurately parse and transcribe born-digital and digitised programmes, achieving over 98% of correct extraction. To overcome the challenges of semantic annotation, we train a reasoning model (POntAvignon) using reinforcement learning with both formal and semantic rewards. This approach enables automated RDF triple generation and supports alignment with existing knowledge graphs. Through a case study based on the Festival d'Avignon corpus, we demonstrate the potential for large-scale, ontology-driven analysis of performing arts data. Our results open new possibilities for interoperable, explainable, and sustainable computational theatre historiography.

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