Triple

T5732019
Position Surface form Disambiguated ID Type / Status
Subject Dogville E126405 entity
Predicate hasCastMember P2308 FINISHED
Object Lauren Bacall E92485 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: Lauren Bacall | Statement: [Dogville, hasCastMember, Lauren Bacall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lauren Bacall
Context triple: [Dogville, hasCastMember, Lauren Bacall]
  • A. Lauren Bacall chosen
    Lauren Bacall was an iconic American film and stage actress known for her sultry voice, striking looks, and classic roles in 1940s Hollywood noir films.
  • B. Ava Gardner
    Ava Gardner was a celebrated American film actress and Hollywood icon of the 1940s and 1950s, renowned for her beauty, charisma, and roles in classics such as "The Killers" and "Mogambo."
  • C. Lizabeth Scott
    Lizabeth Scott was an American film actress known for her sultry voice and frequent roles as a femme fatale in 1940s and 1950s film noir.
  • D. Kim Novak
    Kim Novak is an American actress best known for her roles in classic 1950s and 1960s films, particularly Alfred Hitchcock’s "Vertigo."
  • E. Gloria Grahame
    Gloria Grahame was an American film actress known for her sultry screen presence and acclaimed roles in classic Hollywood films noir and dramas of the 1940s and 1950s.
  • 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_69c0083082288190b7478cead6b5430a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02532fd308190a7434dd42a55e9ac completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c673ea34ec81909b2391a690ed502e completed March 27, 2026, 12:11 p.m.
Created at: March 22, 2026, 3:47 p.m.