Triple

T6621266
Position Surface form Disambiguated ID Type / Status
Subject Lapeer County E149677 entity
Predicate borderedBy P224 FINISHED
Object Livingston County E248139 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: Livingston County | Statement: [Lapeer County, borderedBy, Livingston County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Livingston County
Context triple: [Lapeer County, borderedBy, Livingston County]
  • A. Livingston County chosen
    Livingston County is a largely suburban and rural county in southeastern Michigan known for its growing communities, parks, and proximity to the Detroit and Ann Arbor metropolitan areas.
  • B. Woodward County
    Woodward County is a largely rural county in northwestern Oklahoma known for its agricultural economy and role as a regional trade and service center.
  • C. Lawrence County
    Lawrence County is a county in western Pennsylvania that forms part of the greater Pittsburgh metropolitan region.
  • D. Lawrence County
    Lawrence County is a rural county in northern Alabama known for its agricultural communities, outdoor recreation areas, and proximity to the Tennessee River.
  • E. Lawrence County
    Lawrence County is a rural county in central Mississippi known for its small communities, forests, and agricultural 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_69c687ed8a9c81908bb671717cb192ef completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af7ccaa481908b383b4fd671fa78 completed March 27, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723b26bec819088380fa1b4aff507 completed March 28, 2026, 12:41 a.m.
Created at: March 27, 2026, 1:58 p.m.