Triple

T15610717
Position Surface form Disambiguated ID Type / Status
Subject Rouses Point E375281 entity
Predicate county P75 FINISHED
Object Clinton County E395273 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: Clinton County | Statement: [Rouses Point, county, Clinton County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Clinton County
Context triple: [Rouses Point, county, Clinton County]
  • A. Clinton County chosen
    Clinton County is the name of numerous counties in the United States, typically named after prominent American statesmen such as George Clinton or DeWitt Clinton.
  • B. Greene County
    Greene County is a rural county in southwestern Pennsylvania known for its Appalachian landscape, coal mining history, and small-town communities within the greater Pittsburgh region.
  • C. Greene County
    Greene County is a local government jurisdiction in Tennessee that administers public services and infrastructure, including the Greeneville–Greene County Municipal Airport.
  • D. Greene County
    Greene County is a rural county in eastern New York State known for encompassing a significant portion of the scenic Catskill Mountains.
  • E. Greene County
    Greene County is a rural county in western Alabama known for its historical significance in the Black Belt region and its predominantly African American population.
  • 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_69d85ccf2794819096cda4cbcb02d478 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e8024948190a6c711f2e5c2aac4 completed April 16, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00456b590c8190949fd23cb5cec1e8 completed May 10, 2026, 8:44 a.m.
Created at: April 10, 2026, 4:13 a.m.