This paper investigates the integration of the Linguistic Knowledge Graph (LKG) and Large Language Models (LLMs) for word sense prediction in Latin, a morphologically rich and low-resource historical language. Building on recent work in word sense disambiguation (WSD) and semantic change detection, we use a LKG that integrates information from a diachronic Latin corpus, a sense-annotated dataset of Latin, Latin WordNet, and Wikidata, as a structured representation of semantic and contextual relations. We present sense prediction as a binary classification task over the Latin dataset, using a Graph Retrieval-Augmented Generation approach that combines knowledge graph retrieval with LLM prompting. Two types of graph metadata are tested: author-related information (work, period, occupation) and linguistic metadata (synset and hypernyms derived from WordNet for each word sense). Experiments conducted on GPT-4o-mini, LLaMA-3.1-8B and LLaMA-3.3-70B show varying performance, with F1 scores ranging from 0.53 to 0.77. While GPT-4o-mini achieves the best overall accuracy, LLaMA-3.3-70B benefits the most from graph-based metadata, improving its F1 score by up to 3 points. Analysis by word type reveals that concrete and semantically shifting words are more easily disambiguated than abstract and semantically stable words. Results highlight both the promise and the challenges of combining graph-structured linguistic knowledge with LLMs for historical WSD.

Effective business strategy formulation requires synthesising diverse, often conflicting information sources into coherent action plans. While Large Language Models (LLMs) show potential for processing textual information at scale, their application is limited by hallucinations and a lack of grounding in proprietary data. This paper proposes a methodology that integrates a domain-specific Knowledge Graph (KG) with a GraphRAG pipeline to generate strategic briefing documents, or Primers, which provide a structured overview of a company’s competitive environment. Our approach utilizes an ontology-first framework and Cypher-based graph traversal to capture the relational nature of strategic knowledge beyond simple vector retrieval. Experimental results on a Q&A dataset demonstrate that the Vector + Cypher retrieval strategy significantly improves grounding over LLM-only baselines and outperforms naive vector retrieval in terms of completeness and usefulness. These findings suggest that the synergy of LLMs and structured KGs provides a robust foundation for automated strategic analysis in real-world business scenarios.