Triple

T12402342
Position Surface form Disambiguated ID Type / Status
Subject Harold & Kumar Escape from Guantanamo Bay E296288 entity
Predicate producer P490 FINISHED
Object Nathan Kahane E136662 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: Nathan Kahane | Statement: [Harold & Kumar Escape from Guantanamo Bay, producer, Nathan Kahane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nathan Kahane
Context triple: [Harold & Kumar Escape from Guantanamo Bay, producer, Nathan Kahane]
  • A. Nathan Kahane chosen
    Nathan Kahane is a film producer and studio executive known for backing numerous successful Hollywood comedies and genre films.
  • B. Ranaan Meyer
    Ranaan Meyer is an American double bassist, composer, and founding member of the genre-blending string trio Time for Three.
  • C. Nathan Resnick
    Nathan Resnick is an American entrepreneur best known as the founder and CEO of Sourcify, a platform that helps companies streamline and manage their product manufacturing overseas.
  • D. Jacob Goldman
    Jacob Goldman is a supporting character in the comedy film "Grumpy Old Men," known as a friend and neighbor of the feuding protagonists in their small Minnesota town.
  • E. Neil Kagan
    Neil Kagan is an American editor and author known for producing richly illustrated historical and reference books, particularly for National Geographic.
  • 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d477004819095e65ef6f70c69d9 completed April 10, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63efc0c7081909fe7d1818a081684 completed May 2, 2026, 6:14 p.m.
Created at: April 8, 2026, 9:55 p.m.