Triple

T19237702
Position Surface form Disambiguated ID Type / Status
Subject Town of Groveland E481043 entity
Predicate county P75 FINISHED
Object Livingston County NE NERFINISHED

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: [Town of Groveland, county, Livingston County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Livingston County
Context triple: [Town of Groveland, county, Livingston County]
  • A. Livingston County
    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. Livingston County chosen
    Livingston County is a rural county in western New York State known for its historic villages, agricultural landscape, and proximity to Letchworth State Park.
  • C. 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.
  • D. Lawrence County
    Lawrence County is a rural county in central Mississippi known for its small communities, forests, and agricultural landscape.
  • E. Lawrence County
    Lawrence County is a county in western Pennsylvania that forms part of the greater Pittsburgh metropolitan region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8ccb8f48190ad420098e74fb1db completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5faee7324819090a4c56147cb0bf5 completed April 20, 2026, 10:07 a.m.
Created at: April 10, 2026, 1:26 p.m.