Armenian epigraphy, one of the world’s oldest and most diverse inscriptional traditions, remains largely absent from digital research infrastructures due to a lack of basic linguistic and conceptual resources. No machine-readable corpus, standardized terminology, or controlled vocabulary exists for describing Armenian inscription types, preventing indexing and interoperability. This paper addresses this gap by constructing the first dataset of Armenian inscription-type terminology and by developing a computational pipeline for analyzing it at scale. We digitize and preprocess a broad corpus of authoritative printed publications; curate a culturally grounded terminology list; and train transformer-based NER models to identify both attested inscription types and potential terminological variants across unseen texts. The resulting resources form the first empirical foundation for modelling Armenian epigraphic concepts needed for further developing a SKOS vocabulary aligned with, yet culturally distinct from, existing international epigraphic ontologies.