Triple

T17977316
Position Surface form Disambiguated ID Type / Status
Subject Jamie Rellis E449508 entity
Predicate portrayedBy P1507 FINISHED
Object Mila Kunis NE NERFINISHED

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: Mila Kunis | Statement: [Jamie Rellis, portrayedBy, Mila Kunis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mila Kunis
Context triple: [Jamie Rellis, portrayedBy, Mila Kunis]
  • A. Mila Kunis chosen
    Mila Kunis is an American actress known for her roles in films like "Black Swan" and "Forgetting Sarah Marshall" and for voicing Meg Griffin on the animated series "Family Guy."
  • B. Kaley Cuoco
    Kaley Cuoco is an American actress best known for her comedic television roles, particularly as Penny on the hit sitcom "The Big Bang Theory."
  • C. Tausha Kutcher
    Tausha Kutcher is the older sister of American actor and entrepreneur Ashton Kutcher.
  • D. Zooey Deschanel
    Zooey Deschanel is an American actress, singer, and songwriter known for her quirky, offbeat roles in films like "500 Days of Summer" and the TV series "New Girl."
  • E. Elizabeth Banks
    Elizabeth Banks is an American actress, director, and producer known for her roles in films such as "The Hunger Games" series, "Pitch Perfect," and numerous comedic and dramatic projects in film and television.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f9927c8190a006110c8b996e61 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4b20010c8819088c022565183a7ff completed April 19, 2026, 10:44 a.m.
Created at: April 10, 2026, 10:22 a.m.