Detecting the weaponization of cultural heritage on Wikipedia is not a straightforward classification task. Whether an edit manipulates a historical or cultural narrative often depends on context, interpretation, and the perspectives of those assessing it. We examine this challenge through a study of 400 English-language Wikipedia edits concerning Ukraine and Armenia. Three expert annotators independently classified each edit as Potentially Manipulative, Not Problematic, or Ambiguous, before resolving disagreements through structured discussion. We also compared their judgments with those of a large language model. More than one-third of the edits prompted disagreement, and discussion frequently produced decisions that could not be recovered through majority voting alone. The model aligned more closely with the experts’ deliberated decisions than with their initial majority votes, demonstrating that the construction of “gold” labels can substantially affect how model performance is understood. Our findings also show that Ambiguous captures genuine evidential and interpretive uncertainty rather than annotation failure. We argue that culturally sensitive annotation should preserve disagreement, treat ambiguity as a meaningful outcome, and use deliberation, not mechanical aggregation, to construct reference labels.

Armenian epigraphy is extensively documented in printed scholarly corpora, yet lacks machine-readable editions that support interoperability or computational analysis. In this paper, we present Armenian AutoEpiDoc, a system that automatically converts expert-verified Armenian inscription records into EpiDoc-compliant TEI/XML files. Operating on curated and domain-validated data, AutoEpiDoc maps Armenian-specific metadata to EpiDoc structures through rule-based templates and schema-aware validation. The workflow significantly reduces manual encoding effort and provides a scalable path toward producing digital editions and integrating Armenian inscriptions into international epigraphic infrastructures.

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.