Benchmarking Large Language Models for Geolocating Colonial Virginia Land Grants

Virginia's seventeenth- and eighteenth-century land patents survive primarily as narrative metes-and-bounds descriptions, limiting spatial analysis. This study systematically evaluates current-generation large language models (LLMs) in converting these prose abstracts into geographically accurate latitude/longitude coordinates within a focused evaluation context. A digitized corpus of 5,471 Virginia patent abstracts (1695-1732) is released, with 43 rigorously verified test cases serving as an initial, geographically focused benchmark. Six OpenAI models across three architectures (o-series, GPT-4-class, and GPT-3.5) were tested under two paradigms: direct-to-coordinate and tool-augmented chain-of-thought invoking external geocoding APIs. Results were compared with a GIS-analyst baseline, the Stanford NER geoparser, Mordecai-3, and a county-centroid heuristic.

The top single-call model, o3-2025-04-16, achieved a mean error of 23 km (median 14 km), outperforming the median LLM (37.4 km) by 37.5%, the weakest LLM (50.3 km) by 53.5%, and external baselines by 67% (GIS analyst) and 70% (Stanford NER). A five-call ensemble further reduced errors to 19 km (median 12 km) at minimal additional cost (approx. USD 0.20 per grant), outperforming the median LLM by 48.6%. A patentee-name-redaction ablation increased error by about 9%, indicating reliance on textual landmark and adjacency descriptions rather than memorization. The cost-efficient gpt-4o-2024-08-06 model maintained a 28 km mean error at USD 1.09 per 1,000 grants, establishing a strong cost-accuracy benchmark; external geocoding tools offered no measurable benefit in this evaluation.

These findings demonstrate the potential of LLMs for scalable, accurate, and cost-effective historical georeferencing.