Triple

T6043950
Position Surface form Disambiguated ID Type / Status
Subject Kyle Budwell E134618 entity
Predicate portrayedBy P1507 FINISHED
Object Jack O’Connell E348087 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: Jack O’Connell | Statement: [Kyle Budwell, portrayedBy, Jack O’Connell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jack O’Connell
Context triple: [Kyle Budwell, portrayedBy, Jack O’Connell]
  • A. Jack O’Connell chosen
    Jack O’Connell is an English actor known for his intense performances in film and television, including prominent roles in projects like "Skins," "’71," and "Unbroken."
  • B. Thomas Brodie-Sangster
    Thomas Brodie-Sangster is an English actor known for his roles in projects such as "Nanny McPhee," "The Maze Runner" series, and "Game of Thrones."
  • C. Lewis MacDougall
    Lewis MacDougall is a Scottish actor best known for his breakout role as the young protagonist in the fantasy drama film "A Monster Calls."
  • D. Aidan Chambers
    Aidan Chambers is a British author and critic best known for his innovative and award-winning young adult novels, including the Carnegie Medal–winning "Postcards from No Man’s Land."
  • E. Nicholas Hoult
    Nicholas Hoult is an English actor known for his versatile roles in films such as "About a Boy," the "X-Men" series, and "Mad Max: Fury Road," as well as the TV series "Skins" and "The Great."
  • 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_69c00876a69881908088a2626d3b2666 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056e2b1148190908c4dc43abee266 completed March 22, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11d01aa208190a9c630780f5b9851 completed March 23, 2026, 10:59 a.m.
Created at: March 22, 2026, 4:08 p.m.