Adjectival hypernymy is an underexplored lexical-semantic relation essential for Natural Language Processing (NLP) and hierarchical semantic organization of the lexicon. While hypernymy in nouns and verbs has been extensively modeled in resources such as WordNet, adjectives remain largely unstructured due to their gradability and context-dependence. We present a hybrid Large Language Model (LLM)-Human approach towards the creation of a gold-standard dataset for adjectival hypernymy. Our method integrates three LLMs with systematic human evaluation, guided by a specifically developed theoretical framework ensuring consistency and linguistically-based principles, compiling a resource of 3,836 validated adjective hyponym-hypernym pairs. Results demonstrate high precision for consensus predictions (87%), confirming the utility of cross-model agreement as a proxy for semantic validity. This method highlights how LLMs can complement human effort and expertise to support the construction of lexical resources. The resulting dataset aims to enrich the Open English WordNet (OEWN) with explicit adjectival hierarchies and serves as a benchmark for hypernymy detection and lexical entailment evaluation.

This paper presents the first core component of LinkEn, a knowledge base of interoperable language resources for English adhering to Linked Open Data principles. With this initial step towards a broader infrastructure, we focus on the development of a lemma-centered hub designed to enable interoperability between distributed lexical resources, corpora, and linguistic annotations. The modeling is inspired by the LiLa Knowledge Base for Latin and the OntoLex-Lemon model, ensuring compatibility with existing lemma-centric knowledge graphs and enabling future cross-linguistic interoperability. Rather than relying solely on manual knowledge graph construction and significant human effort, the lemma bank has been developed through a hybrid neuro-symbolic pipeline that integrates large language models into the generation of RDF data under explicit ontological constraints. This approach combines automated generation with ontology-driven supervision and evaluation, enabling scalable yet controlled construction of structured lexical knowledge. By presenting the first steps towards the LinkEn Knowledge Base, this paper contributes both a new lemma bank for English and an experimental methodology for the semi-automatic creation of Linked Data based knowledge graphs.

Open English Wordnet is a key resource published in OntoLex-lemon as part of the linguistic linked open data cloud. There are, however, many links missing in the resource, and in this paper, we look at how we can establish hyper-ymy between adjectives. We present a theoretical discussion of the hypernymy relation and how it differs for adjectives in contrast to nouns and verbs. We develop a new resource for adjective hypernymy and fine-tune large language models to predict adjective hypernymy, showing that the methodology of TaxoLLaMa can be adapted to this task.