PREMOVE is a diachronic dataset of Ancient Greek and Latin PREverbed MOtion VErbs, providing manually curated morphological, syntactic, and semantic annotations for almost three thousand verbal occurrences. This paper presents the integration of PREMOVE into the LiLa Knowledge Base of Latin, linking its semantic annotations to WordNet (WN) and VerbNet (VN). We describe the RDF conversion using OntoLex-Lemon and FrAC, enabling explicit modelling of token-level attestations and dataset-level provenance. The resulting linked resource achieves full FAIR compliance and supports complex SPARQL queries, allowing users to explore motion semantics across lexical, textual, and semantic layers. Example SPARQL queries demonstrate how researchers can retrieve attested forms for specific WN synsets or VN classes, supporting reproducible linguistic research and cross-resource exploration of motion semantics in ancient languages.

This paper investigates the lexicalisation of geographical nouns in Latin and Ancient Greek using a nd Ancient Greek using a diachronic, multi-genre corpus (8th cent. BCE – 2nd cent. CE) and Large Language Models for Word Sense Disambiguation. We focus on two main aspects: the onomasiological question of which words encode core geographical concepts, and the semasiological distribution of senses across lemmas. Across both languages, city-related concepts are the most frequently expressed, but Greek shows a stronger focus on maritime terms, whereas Latin favours concepts related to land. Semasiologically, Latin shows clearer evidence of semantic change over time (e.g., ’citizenship’ - ’city’, aequor ’flat surface’ - ’sea’), while Greek displays more gradual or distributed shifts. These results show that computational annotation enables cross-linguistic and diachronic analysis of spatial semantics, allowing us to compare the frequency of concepts across languages, genres, and periods, and to track when semantic change occurs and how core concepts evolve over time.