Triple

T23511069
Position Surface form Disambiguated ID Type / Status
Subject Jason Schwartzman E572421 entity
Predicate notableWork P4 FINISHED
Object Rushmore 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: Rushmore | Statement: [Jason Schwartzman, notableWork, Rushmore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rushmore
Context triple: [Jason Schwartzman, notableWork, Rushmore]
  • A. Rushmore chosen
    Rushmore is a 1998 Wes Anderson coming-of-age comedy film starring Jason Schwartzman and Bill Murray, known for its offbeat humor, distinctive visual style, and deadpan performances.
  • B. Hancock
    Hancock is a small village in central Wisconsin known for its rural character and surrounding lakes and forests.
  • C. Hancock
    Hancock is a small rural town in western Massachusetts known for its scenic Berkshire landscapes and outdoor recreation.
  • D. Hancock
    Hancock is a small city in Michigan’s Upper Peninsula known for its Finnish-American heritage and proximity to Lake Superior.
  • E. Hancock
    Hancock is a British television sitcom starring Tony Hancock that became a landmark of 1950s–60s UK comedy for its character-driven humor and influential writing.
  • 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_69e245b5e4208190bac8a6509867e394 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1aa7e99b081909620c4f951100023 completed April 29, 2026, 6:51 a.m.
Created at: April 17, 2026, 6:07 p.m.