Triple

T11163688
Position Surface form Disambiguated ID Type / Status
Subject Oxford (federal electoral district) E264104 entity
Predicate geographicRegion P285 FINISHED
Object Oxford County E259083 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Oxford County | Statement: [Oxford (federal electoral district), geographicRegion, Oxford County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oxford County
Context triple: [Oxford (federal electoral district), geographicRegion, Oxford County]
  • A. Oxford County
    Oxford County is a largely rural county in western Maine known for its small towns, forests, and outdoor recreation areas.
  • B. Oxford County chosen
    Oxford County is a regional municipality in southwestern Ontario, Canada, known for its agricultural communities and the city of Woodstock as its largest urban center.
  • C. Windsor County
    Windsor County is a county in southeastern Vermont known for its historic towns, rural landscapes, and role as a regional center in the Upper Connecticut River Valley.
  • D. Auglaize County
    Auglaize County is a county in western Ohio known for its agricultural communities and its county seat, Wapakoneta.
  • E. Lebanon County
    Lebanon County is a county in south-central Pennsylvania known for its mix of rural farmland, small towns, and historical communities such as the city of Lebanon.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8832fe88190a74d81f9ed547baa completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e463851a348190a9a8bd1501026d83 completed April 19, 2026, 5:09 a.m.
Created at: April 8, 2026, 9:29 p.m.