Triple

T8504521
Position Surface form Disambiguated ID Type / Status
Subject Green Country E201300 entity
Predicate containsCounty P5971 FINISHED
Object Wagoner County E281083 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: Wagoner County | Statement: [Green Country, containsCounty, Wagoner County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wagoner County
Context triple: [Green Country, containsCounty, Wagoner County]
  • A. Wagoner County chosen
    Wagoner County is a county in northeastern Oklahoma that includes part of the city of Broken Arrow and is part of the Tulsa metropolitan area.
  • B. Garvin County
    Garvin County is a county in south-central Oklahoma known for its agricultural economy, small towns, and location within the state's oil and gas region.
  • C. Bonner County
    Bonner County is a largely rural county in northern Idaho known for its forests, lakes, and outdoor recreation areas, including parts of Lake Pend Oreille and the Selkirk Mountains.
  • D. Stephens County
    Stephens County is a county in northeastern Georgia, United States, known for its location in the Appalachian foothills and its seat, the city of Toccoa.
  • E. Crane County
    Crane County is a sparsely populated county in western Texas known for its oil production and rural desert landscape.
  • 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_69ca831fe47c8190b5c57b456d2aefa0 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe59d67d081908155a43b9b463fe3 completed March 31, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce88e0e08c819085d29157349a6ef6 completed April 2, 2026, 3:18 p.m.
Created at: March 30, 2026, 6:14 p.m.