Triple

T12424946
Position Surface form Disambiguated ID Type / Status
Subject Point Blank E296872 entity
Predicate producer P490 FINISHED
Object Jud Kinberg E1009437 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: Jud Kinberg | Statement: [Point Blank, producer, Jud Kinberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jud Kinberg
Context triple: [Point Blank, producer, Jud Kinberg]
  • A. Jud Kinberg chosen
    Jud Kinberg was an American film producer known for his work on a range of feature films, including the psychological thriller "The Collector."
  • B. Michael Kagan
    Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
  • C. Scott Einbinder
    Scott Einbinder is a film producer best known for his work on the dark crime thriller "Killer Joe."
  • D. 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.
  • E. Martin Weinberg
    Martin Weinberg is a sociologist known for his influential research on human sexuality, sexual deviance, and the social construction of sexual norms.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d7b6bd08190b30beba393a5b1e7 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7c6f1e29c8190b073c3293cf68cb2 completed May 3, 2026, 10:06 p.m.
Created at: April 8, 2026, 9:55 p.m.