Learning AS-to-Organization Mappings with Borges

We introduce Borges (Better ORGanizations Entities mappingS), a novel framework for improving AS-to-Organization mappings using Large Language Models (LLMs). Existing approaches, such as AS2Org and its extensions, rely on static WHOIS data and rule-based extraction from PeeringDB records, limiting their ability to capture complex, dynamic organizational structures. Borges overcomes these limitations by combining traditional sources with few-shot LLM prompting to extract sibling relationships from free-text fields in PeeringDB, and by introducing website-based inference using redirect chains, domain similarity, and favicon analysis. Our evaluation shows that Borges outperforms prior methods, achieving a 7% improvement in sibling ASN identification and an Organization Factor score of 0.3576. It also expands the recognized user base of large Internet conglomerates by 192 million users (≈ 5% of the global Internet population) and improves geographic footprint estimates across multiple regions.