Triple

T2422174
Position Surface form Disambiguated ID Type / Status
Subject John Cassavetes E53441 entity
Predicate directed P7373 FINISHED
Object Gloria E265077 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: Gloria | Statement: [John Cassavetes, directed, Gloria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gloria
Context triple: [John Cassavetes, directed, Gloria]
  • A. Gloria chosen
    Gloria is a 1980 American crime drama film written and directed by John Cassavetes, starring Gena Rowlands as a tough ex-mobster’s girlfriend protecting a young boy from gangsters.
  • B. Gloria
    Gloria is a joyful hymn of praise in Christian liturgy, traditionally sung during major celebrations such as the Easter Vigil.
  • C. Gloria
    Gloria is a central human character in the 2023 film "Barbie," portrayed as a Mattel employee and mother whose personal struggles and imagination help bridge the real world with Barbie Land.
  • D. Gloria
    Gloria is an American sitcom centered on Gloria Stivic, the daughter from "All in the Family," as she navigates life as a single mother.
  • E. Marlene
    Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc971093481909c8924d58187860c completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0a299648190a55e9f2c47bd307f completed March 9, 2026, 4:09 p.m.
Created at: March 6, 2026, 9:42 p.m.