Triple

T10467400
Position Surface form Disambiguated ID Type / Status
Subject V for Vendetta E246831 entity
Predicate leadActor P1507 FINISHED
Object Natalie Portman E49597 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: Natalie Portman | Statement: [V for Vendetta, leadActor, Natalie Portman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Natalie Portman
Context triple: [V for Vendetta, leadActor, Natalie Portman]
  • A. Natalie Portman chosen
    Natalie Portman is an acclaimed Israeli-American actress known for her versatile film roles and her Academy Award-winning performance in "Black Swan."
  • B. Mia Wasikowska
    Mia Wasikowska is an Australian actress known for her versatile performances in films such as "Alice in Wonderland," "Jane Eyre," and various independent dramas.
  • C. Kate Bosworth
    Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
  • D. Maggie Gyllenhaal
    Maggie Gyllenhaal is an American actress known for her nuanced performances in independent films and major studio productions, including her acclaimed role as Rachel Dawes in Christopher Nolan’s Batman franchise.
  • E. Arielle Kebbel
    Arielle Kebbel is an American actress and former model known for her roles in film and television series such as "The Vampire Diaries," "Gilmore Girls," and various romantic comedies and dramas.
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5092e3230819098ab444f73c9bd40 completed April 7, 2026, 1:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d89ff1cd948190a1ef331fb810bf26 completed April 10, 2026, 7 a.m.
Created at: April 6, 2026, 12:20 p.m.