Triple

T6813129
Position Surface form Disambiguated ID Type / Status
Subject Bell, Book and Candle E156684 entity
Predicate starring P1507 FINISHED
Object Kim Novak E414298 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: Kim Novak | Statement: [Bell, Book and Candle, starring, Kim Novak]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kim Novak
Context triple: [Bell, Book and Candle, starring, Kim Novak]
  • A. Kim Novak chosen
    Kim Novak is an American actress best known for her roles in classic 1950s and 1960s films, particularly Alfred Hitchcock’s "Vertigo."
  • 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. 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. Sylvia Sidney
    Sylvia Sidney was an American actress known for her work in 1930s crime dramas and later roles in films like "Beetlejuice" and "Mars Attacks!".
  • E. Dina Merrill
    Dina Merrill was an American actress, heiress, and philanthropist known for her elegant screen presence in mid-20th-century Hollywood films and television.
  • 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_69c68828b26c819090fe9df7612bbc27 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d32b012481909b73784899ec2a0d completed March 27, 2026, 6:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c80299ee8c8190a64397339ae62119 completed March 28, 2026, 4:32 p.m.
Created at: March 27, 2026, 2:17 p.m.