Between the Lines: Leveraging LLMs for Information Extraction of Subsea Data Cable News

This paper investigates the potential of Large Language Models (LLMs) in automating the extraction and analysis of Subsea Data Cables (SDCs)-related information from unstructured media sources. A comprehensive LLM-based information extraction module was developed and integrated into an existing SDC database. By systematically comparing different LLMs, including GPT-4o, Gemini 1.5 Flash, Claude 3.5 Sonnet, and Llama 3.1, this work identifies optimal model configurations for SDC news processing, considering accuracy, hallucination rate, and processing speed. The system implements Claude 3.5 Sonnet and GPT4-o as primary models, incorporating domain-specific prompt engineering and robust output validation mechanisms. Performance evaluation demonstrates substantial improvements over rule-based methods. While excelling at natural language understanding tasks, the system revealed limitations in extracting more specific technical details such as construction costs and capacity measurements. The implementation provides a modular, adaptable framework for automated information extraction in specialised technical domains. The results demonstrate that LLMs, when properly implemented with structured prompts and validation mechanisms, can significantly enhance the automated monitoring and analysis of SDC-related events.