Triple

T13603313
Position Surface form Disambiguated ID Type / Status
Subject Skin (2018 film) E324996 entity
Predicate producer P490 FINISHED
Object Oren Moverman E102599 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: Oren Moverman | Statement: [Skin (2018 film), producer, Oren Moverman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oren Moverman
Context triple: [Skin (2018 film), producer, Oren Moverman]
  • A. Oren Moverman chosen
    Oren Moverman is an Israeli-American screenwriter and director known for his work on acclaimed films such as "I'm Not There," "The Messenger," and "Rampart."
  • B. Des McAnuff
    Des McAnuff is a Tony Award–winning Canadian-American director and producer best known for his work on major Broadway musicals such as "Jersey Boys" and "Big River."
  • C. J. C. Chandor
    J. C. Chandor is an American filmmaker known for writing and directing character-driven dramas and thrillers such as "Margin Call," "All Is Lost," and "A Most Violent Year."
  • D. Todd Field
    Todd Field is an American filmmaker, screenwriter, and former actor best known for directing critically acclaimed films such as "In the Bedroom," "Little Children," and "Tár."
  • E. Martin Arjovsky
    Martin Arjovsky is a machine learning researcher best known for introducing the Wasserstein GAN, a generative adversarial network variant that improves training stability and sample quality.
  • 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_69d80769eaf081909d82f44e484d6113 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb07ca07481909c45da551ea61ab4 completed April 12, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ae394748190b6a0f9a085b7dea6 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:49 p.m.