Triple

T20582620
Position Surface form Disambiguated ID Type / Status
Subject Kansas City Chiefs Hall of Fame E505697 entity
Predicate notableInductee P7102 FINISHED
Object Jan Stenerud 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: Jan Stenerud | Statement: [Kansas City Chiefs Hall of Fame, notableInductee, Jan Stenerud]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jan Stenerud
Context triple: [Kansas City Chiefs Hall of Fame, notableInductee, Jan Stenerud]
  • A. Jan Stenerud chosen
    Jan Stenerud is a Norwegian-born Pro Football Hall of Fame placekicker renowned as one of the first great specialist kickers in NFL history.
  • B. Leif Tronstad
    Leif Tronstad was a Norwegian scientist, resistance leader, and military officer who played a key role in planning and directing Allied operations against Nazi Germany’s nuclear program during World War II.
  • C. Roland Swenson
    Roland Swenson is an American music and media executive best known as a co-founder and longtime leader of the South by Southwest (SXSW) festival in Austin, Texas.
  • D. Kurt Johnstad
    Kurt Johnstad is an American screenwriter best known for writing the action films "300" and "Atomic Blonde."
  • E. Dan Paulson
    Dan Paulson is a film and television producer best known for his work on action films like "Passenger 57."
  • 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_69e0b4b9669c8190b8e81fc72817d42c completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a90f7e34819086b1745dcd53ba25 completed April 20, 2026, 10:30 p.m.
Created at: April 16, 2026, 11:40 a.m.