Triple

T11319726
Position Surface form Disambiguated ID Type / Status
Subject Stephen E. Rivkin E268059 entity
Predicate collaboratedWith P435 FINISHED
Object Michael Kahn E255464 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: Michael Kahn | Statement: [Stephen E. Rivkin, collaboratedWith, Michael Kahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kahn
Context triple: [Stephen E. Rivkin, collaboratedWith, Michael Kahn]
  • A. Michael Kahn chosen
    Michael Kahn is an acclaimed American film editor best known for his long-time collaboration with director Steven Spielberg on numerous major films.
  • B. Tom Kahn
    Tom Kahn was an American social democrat and civil rights activist known for his work with the AFL-CIO and his role in organizing the 1963 March on Washington.
  • C. Mitch Kertzman
    Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
  • D. Ian Kahn
    Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
  • E. Ben Karlin
    Ben Karlin is an American television writer and producer best known for his work on The Daily Show and The Colbert Report.
  • 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_69d6aaca5c24819083db46a30d86cb34 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9de875481908acfa56015d4b46f completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e525e2549081909ec99e4c7006fd66 completed April 19, 2026, 6:58 p.m.
Created at: April 8, 2026, 9:32 p.m.