This paper describes the organisation and results of the Named Entity Recognition and Classification (NERC) shared task, conducted as part of EvaLatin 2026. The fourth edition of this evaluation campaign for Natural Language Processing on Latin features two shared tasks, i.e. Dependency Parsing and NERC. After introducing the objective of the task and presenting the Ancient Named Entities Special Interest Group, which aims to address the specific challenges that this task presents, this overview details the annotation tagset, the data provided to the participants and their format. The evaluation metrics and the scorer are also described. Finally, the methodology used by each participating team and their results are presented and discussed.

This paper considers Named Entity Linking for person mentions from classical Latin texts to a domain-specific, German language knowledge base, namely Paulys RealencyclopΣdie. Following a methodology similar to (anonymous_reference), we train a transformer-based, retrieval and ranking model (BLINK) first on a general, Wikipedia-derived dataset and subsequently on a more specific dataset, gathered from various sources, linking to our target knowledge base. Results show that while BLINK performs well on mention-entity pairs linked to entities seen during training, it performs significantly worse on mention-entity pairs linking to unseen entities. We provide a detailed error analysis, propose possible exploitation strategies for a human-in-the-loop approach, and identify directions for future improvement.

This paper describes the release as Linguistic Linked Open Data of DynaMorphPro, a lexical resource recording loanwords, conversions and class-shifts from Latin to Old Italian. We show how existing vocabularies are reused and integrated to allow for a rich semantic representation of these data. Our main reference is the OntoLex-lemon model for lexical information, but classes and properties from many other ontologies are also reused to express other aspects. In particular, we identify the CIDOC Concept Reference Model as the ideal tool to convey chronological information on historical processes of lexical innovation and change, and describe how it can be integrated with OntoLex-lemon.

This paper presents two use cases of the etymological data provided by the Lexicon der indogermanischen Verben (LIV) after their publication as Linked Open Data and their linking to the LiLa Knowledge Base (KB) of interoperable linguistic resources for Latin. The first part of the paper briefly describes the LiLa KB and its structure. Then, the LIV and the information it contains are introduced, followed by a short description of the ontologies and the extensions used for modelling the LIV’s data and interlinking them to the LiLa ecosystem. The last section details the two use cases. The first case concerns the inflection types of the Latin verbs that reflect Proto-Indo-European stems, while the second one focusses on the Latin derivatives of the inherited stems. The results of the investigations are put in relation to current research topics in Historical Linguistics, demonstrating their relevance to the discipline.