Triple

T15389246
Position Surface form Disambiguated ID Type / Status
Subject Dreamer E367995 entity
Predicate starring P1507 FINISHED
Object Oded Fehr E502077 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: Oded Fehr | Statement: [Dreamer, starring, Oded Fehr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oded Fehr
Context triple: [Dreamer, starring, Oded Fehr]
  • A. Oded Fehr chosen
    Oded Fehr is an Israeli actor best known for his roles in action and horror films such as The Mummy series and the Resident Evil franchise.
  • B. Ehud Kalai
    Ehud Kalai is an Israeli-American game theorist and economist known for his influential contributions to bargaining theory, game theory, and economic theory.
  • C. Oded Kotler
    Oded Kotler is an Israeli actor and theater director known for his prominent roles in film and stage as well as his influential work in Israeli performing arts.
  • D. Assaf Naor
    Assaf Naor is an Israeli mathematician renowned for his work in functional analysis, metric geometry, and theoretical computer science.
  • E. Shmuel Safra
    Shmuel Safra is an Israeli theoretical computer scientist known for his influential work in computational complexity theory, including foundational contributions to probabilistically checkable proofs and hardness of approximation.
  • 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_69d85a1551a08190ba2caea7cd51c639 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e761b688190893a81246b735b76 completed April 16, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff134e37d881909f373b90a99fc067 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:19 a.m.