Triple

T16949834
Position Surface form Disambiguated ID Type / Status
Subject Hobbs, New Mexico E411153 entity
Predicate county P75 FINISHED
Object Lea County E58223 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: Lea County | Statement: [Hobbs, New Mexico, county, Lea County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lea County
Context triple: [Hobbs, New Mexico, county, Lea County]
  • A. Lea County chosen
    Lea County is a largely rural, oil- and gas-producing county in southeastern New Mexico known for its energy industry and agricultural activities.
  • B. Fisher County
    Fisher County is a rural county in west-central Texas known for its agricultural economy and small, sparsely populated communities.
  • C. Mayes County
    Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
  • D. Sherman County
    Sherman County is a sparsely populated rural county in the far northern part of the Texas Panhandle, known for its agriculture and wide-open High Plains landscape.
  • E. Sherman County
    Sherman County is a rural county in central Nebraska known for its agricultural landscape and small communities.
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3cfb570b08190ae3385b0cad32668 completed April 18, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00cfeeee948190a5c8904d1f559a28 completed May 10, 2026, 6:35 p.m.
Created at: April 10, 2026, 5:31 a.m.