Triple

T14287721
Position Surface form Disambiguated ID Type / Status
Subject The Time Warrior E354217 entity
Predicate starsActor P5563 FINISHED
Object June Brown E488990 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: June Brown | Statement: [The Time Warrior, starsActor, June Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: June Brown
Context triple: [The Time Warrior, starsActor, June Brown]
  • A. June Brown chosen
    June Brown was a renowned English actress best known for her long-running role as Dot Cotton on the BBC soap opera "EastEnders."
  • B. Louise Plowright
    Louise Plowright was a British actress known for her work in television and musical theatre, including notable roles in West End productions.
  • C. Beatrice Arthur
    Beatrice Arthur was an American actress and comedian best known for her sharp-tongued, iconic roles on the television sitcoms "Maude" and "The Golden Girls."
  • D. Phyllis Loughton
    Phyllis Loughton was an American actress and acting teacher best known for her work on stage and for her marriage to filmmaker George Seaton.
  • E. Frances Barber
    Frances Barber is an English actress known for her extensive work in film, television, and theatre, including roles in productions such as "Film Stars Don’t Die in Liverpool."
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de698023288190b1d705235c2b2ca3 completed April 14, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1e14d4819091c381f96c43c58b completed May 8, 2026, 1:32 a.m.
Created at: April 10, 2026, 1:11 a.m.