Triple

T1532948
Position Surface form Disambiguated ID Type / Status
Subject Ava Gardner E32485 entity
Predicate name P16 FINISHED
Object Ava Gardner E32485 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: Ava Gardner | Statement: [Ava Gardner, name, Ava Gardner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ava Gardner
Context triple: [Ava Gardner, name, Ava Gardner]
  • A. Ava Gardner chosen
    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."
  • B. 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.
  • C. Lauren Bacall
    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.
  • D. Lana Turner
    Lana Turner was a glamorous American film actress and iconic Hollywood star of the 1940s and 1950s, renowned for her dramatic roles and enduring screen presence.
  • E. Anne Baxter
    Anne Baxter was an American actress known for her Academy Award–winning and nominated performances in classic films such as "The Razor's Edge," "All About Eve," and "The Ten Commandments."
  • 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_69a885ea86308190998f6bc14bb91f8e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9081835e4819093dee004fdb027ff completed March 5, 2026, 4:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc98b2b0081909d10b22d59c5e653 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:26 p.m.