Triple

T3203081
Position Surface form Disambiguated ID Type / Status
Subject Queen Guinevere E67095 entity
Predicate portrayedBy P1507 FINISHED
Object Keira Knightley E53444 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: Keira Knightley | Statement: [Queen Guinevere, portrayedBy, Keira Knightley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Keira Knightley
Context triple: [Queen Guinevere, portrayedBy, Keira Knightley]
  • A. Keira Knightley chosen
    Keira Knightley is an English actress known for her roles in period dramas and major film franchises such as "Pirates of the Caribbean" and "Pride & Prejudice."
  • B. Gemma Arterton
    Gemma Arterton is an English actress known for her roles in films such as "St Trinian's," "Quantum of Solace," and "Prince of Persia: The Sands of Time."
  • C. Emily Watson
    Emily Watson is an acclaimed English actress known for her powerful performances in films such as "Breaking the Waves," "Hilary and Jackie," and "Punch-Drunk Love."
  • D. Sophie Fiennes
    Sophie Fiennes is a British film director and producer known for her innovative documentaries and collaborations with artists and philosophers.
  • E. Kate Winslet
    Kate Winslet is an acclaimed English actress known for her versatile performances in films such as "Titanic," "Eternal Sunshine of the Spotless Mind," and "The Reader," for which she has received numerous major awards.
  • 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_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada9b188a88190b7b5e9b3be9410db completed March 8, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bc7772c8190b286141dac5ea778 completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:07 p.m.