Triple

T15482104
Position Surface form Disambiguated ID Type / Status
Subject Moore County E376943 entity
Predicate borderedBy P224 FINISHED
Object Hartley County E380046 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: Hartley County | Statement: [Moore County, borderedBy, Hartley County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hartley County
Context triple: [Moore County, borderedBy, Hartley County]
  • A. Hartley County chosen
    Hartley County is a sparsely populated rural county in the northwestern Texas Panhandle known for its ranching and agricultural economy.
  • B. Terry County
    Terry County is a rural county in western Texas known for its agriculture, particularly cotton farming, and its location on the South Plains region.
  • C. Mayes County
    Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
  • D. Meade County
    Meade County is a county in western South Dakota known for its paleontological sites and proximity to the Black Hills region.
  • E. Meade County
    Meade County is a county-level jurisdiction in the U.S. state of Kentucky, with Brandenburg serving as its county seat.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f8cb4388190a3b4c92c3bb4ad4f completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb7921208190bbf4e1a01c6ec5ee completed May 10, 2026, 2:20 a.m.
Created at: April 10, 2026, 3:38 a.m.