Triple

T9906709
Position Surface form Disambiguated ID Type / Status
Subject Shallow Hal E185028 entity
Predicate starring P1507 FINISHED
Object Gwyneth Paltrow E63076 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: Gwyneth Paltrow | Statement: [Shallow Hal, starring, Gwyneth Paltrow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gwyneth Paltrow
Context triple: [Shallow Hal, starring, Gwyneth Paltrow]
  • A. Gwyneth Paltrow chosen
    Gwyneth Paltrow is an American actress and businesswoman best known for her Academy Award–winning performance in "Shakespeare in Love" and for playing Pepper Potts in the Marvel Cinematic Universe.
  • B. Gwyneth Williams
    Gwyneth Williams is a British media executive best known for her role as Controller of BBC Radio 4 and BBC Radio 4 Extra.
  • C. Reese Witherspoon
    Reese Witherspoon is an American actress and producer known for her versatile roles in film and television, including "Legally Blonde," "Walk the Line," and "Big Little Lies."
  • D. Jessica Alba
    Jessica Alba is an American actress and businesswoman known for her roles in films like "Fantastic Four" and for founding the consumer goods company The Honest Company.
  • E. Rose Byrne
    Rose Byrne is an Australian actress known for her versatile performances in films such as Bridesmaids, Neighbors, and X-Men: First Class, as well as the TV series Damages.
  • 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_69ca8296165881908ca4750701af1f29 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb50cf8808190a41e565216712704 completed April 2, 2026, 12:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eb346c7081908300e54cd639b027 completed April 5, 2026, 4:55 a.m.
Created at: March 30, 2026, 8:41 p.m.