Triple

T4085332
Position Surface form Disambiguated ID Type / Status
Subject Kansas City Kansas Community College E87575 entity
Predicate county P75 FINISHED
Object Wyandotte County E98574 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: Wyandotte County | Statement: [Kansas City Kansas Community College, county, Wyandotte County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wyandotte County
Context triple: [Kansas City Kansas Community College, county, Wyandotte County]
  • A. Wyandotte County chosen
    Wyandotte County is an urban county in northeastern Kansas that includes and is largely defined by the city of Kansas City, Kansas.
  • B. Dewey County
    Dewey County is a rural county in northwestern Oklahoma known for its agricultural economy and small-town communities.
  • C. Wayne County
    Wayne County is a county in western New York State, situated along the southern shore of Lake Ontario and known for its agricultural production and small rural communities.
  • D. Wayne County
    Wayne County is a populous county in southeastern Michigan that includes the city of Detroit and serves as a major industrial and cultural hub of the state.
  • E. Wayne County
    Wayne County is a county in southeastern Georgia known for its rural communities, forestry, and transportation routes connecting coastal and inland parts of the state.
  • 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_69aed9435cf48190ad1da737c962d19d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefc7b7cc4819089cfbf2b1c23ccc5 completed March 9, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b562c9760c8190a9292eb1cea55ab2 completed March 14, 2026, 1:29 p.m.
Created at: March 9, 2026, 3:39 p.m.