Triple

T1046397
Position Surface form Disambiguated ID Type / Status
Subject The Harvey Girls E22588 entity
Predicate starring P1507 FINISHED
Object Ray Bolger E48998 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: Ray Bolger | Statement: [The Harvey Girls, starring, Ray Bolger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ray Bolger
Context triple: [The Harvey Girls, starring, Ray Bolger]
  • A. Ray Bolger chosen
    Ray Bolger was an American actor, singer, and dancer best known for his iconic role as the Scarecrow in the classic 1939 film "The Wizard of Oz."
  • B. Bela Lugosi
    Bela Lugosi was a Hungarian-American actor best known for his iconic portrayal of Count Dracula in early horror cinema.
  • C. Christopher Lee
    Christopher Lee was an English actor renowned for his deep voice and imposing presence, best known for iconic roles such as Count Dracula in Hammer Horror films and Saruman in "The Lord of the Rings" trilogy.
  • D. Murray Hamilton
    Murray Hamilton was an American character actor best known for playing the stubborn Mayor Larry Vaughn in the classic thriller film "Jaws."
  • E. Christopher Walken
    Christopher Walken is an American actor known for his distinctive voice, eccentric screen presence, and memorable roles in films such as "The Deer Hunter," "Pulp Fiction," and "Catch Me If You Can."
  • 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_69a493d91478819094cc01fb65564bc1 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b84bb0048190badf6d2f7f684d99 completed March 1, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac429cc3c481909c55459790d6857f completed March 7, 2026, 3:22 p.m.
Created at: March 1, 2026, 7:42 p.m.