Triple

T9940993
Position Surface form Disambiguated ID Type / Status
Subject Jean Louis E194079 entity
Predicate designedFor P98 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: [Jean Louis, designedFor, Kim Novak]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kim Novak
Context triple: [Jean Louis, designedFor, 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. Lesley Caron
    Lesley Caron is a British theatre director known for her work on stage productions and for being married to fellow director Peter Hall.
  • C. 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."
  • D. 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.
  • E. Jean Kerr
    Jean Kerr was an American author and playwright best known for her humorous essays and plays about suburban family life, including the bestseller "Please Don’t Eat the Daisies."
  • 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_69ca82e409348190a393777356b80a2a completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb60f4ffc8190bfe916bb4a7bf5c5 completed April 2, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e531dfa48190b57fcd2444de1ab7 completed April 5, 2026, 10:41 p.m.
Created at: March 30, 2026, 8:44 p.m.