Triple

T19233717
Position Surface form Disambiguated ID Type / Status
Subject Kitna E480937 entity
Predicate notableBearer P458 FINISHED
Object Jon Kitna 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: Jon Kitna | Statement: [Kitna, notableBearer, Jon Kitna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jon Kitna
Context triple: [Kitna, notableBearer, Jon Kitna]
  • A. Jon Kitna chosen
    Jon Kitna is a former NFL quarterback best known for his starting stints with the Seattle Seahawks, Cincinnati Bengals, and Detroit Lions during a 16-year professional career.
  • B. Mark Rypien
    Mark Rypien is a former NFL quarterback best known for leading the Washington Redskins to a championship and earning Super Bowl XXVI’s Most Valuable Player honors.
  • C. Warren Moon
    Warren Moon is a Hall of Fame quarterback renowned for his prolific passing career in both the Canadian Football League and the NFL.
  • D. Bob Griese
    Bob Griese is a Hall of Fame former NFL quarterback for the Miami Dolphins who later became a prominent football television analyst.
  • E. Calvin Palmer Jr.
    Calvin Palmer Jr. is the central protagonist of the "Barbershop" film series, a young Chicago barbershop owner balancing family, community, and the challenges of running a small business.
  • 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_69e5faeb53988190b83afee9974058c6 completed April 20, 2026, 10:07 a.m.
Created at: April 10, 2026, 1:26 p.m.