Triple

T15080629
Position Surface form Disambiguated ID Type / Status
Subject Steve King E380129 entity
Predicate succeededBy P78 FINISHED
Object Randy Feenstra E418064 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: Randy Feenstra | Statement: [Steve King, succeededBy, Randy Feenstra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Randy Feenstra
Context triple: [Steve King, succeededBy, Randy Feenstra]
  • A. Randy Feenstra chosen
    Randy Feenstra is a Republican politician and U.S. Representative from Iowa known for his conservative positions on agriculture, fiscal policy, and social issues.
  • B. Randy Mueller
    Randy Mueller is an American football executive known for serving as a general manager for multiple professional teams, including in the NFL and the Alliance of American Football.
  • C. Randy Steckle
    Randy Steckle is a central medical student character in the 1990 psychological horror film "Flatliners," involved in dangerous experiments with near-death experiences.
  • D. Todd Boekelheide
    Todd Boekelheide is an American film composer and sound editor known for his work on numerous documentaries and feature films, including Academy Award–winning projects.
  • E. Kevin Nolting
    Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dff80008c88190840f94222f867478 completed April 15, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff4c2d21348190a8045cf1847396e0 completed May 9, 2026, 3:01 p.m.
Created at: April 10, 2026, 3:03 a.m.