  Given the wide use of forgery throughout history, scholars have and are
continuously engaged in assessing the authenticity of historical documents.
However, online catalogues merely offer descriptive metadata for these
documents, relegating discussions about their authenticity to free-text
formats, making it difficult to study these assessments at scale. This study
explores the generation of structured data about documents' authenticity
assessment from natural language texts. Our pipeline exploits Large Language
Models (LLMs) to select, extract and classify relevant claims about the topic
without the need for training, and Semantic Web technologies to structure and
type-validate the LLM's results. The final output is a catalogue of documents
whose authenticity has been debated, along with scholars' opinions on their
authenticity. This process can serve as a valuable resource for integration
into catalogues, allowing room for more intricate queries and analyses on the
evolution of these debates over centuries.
