Triple

T17546953
Position Surface form Disambiguated ID Type / Status
Subject Marie Ault E427352 entity
Predicate name P16 FINISHED
Object Marie Ault NE NERFINISHED

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: Marie Ault | Statement: [Marie Ault, name, Marie Ault]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marie Ault
Context triple: [Marie Ault, name, Marie Ault]
  • A. Marie Ault chosen
    Marie Ault was a British character actress of stage and screen, best remembered for her roles in early 20th-century British cinema, including Alfred Hitchcock’s silent films.
  • B. Marie De Forest
    Marie De Forest, better known as Marie Mosquini, was an American silent film actress who frequently appeared in Hal Roach comedies during the 1910s and 1920s.
  • C. Elma Milotte
    Elma Milotte was an American cinematographer and filmmaker best known for her wildlife photography work on early nature documentaries for Walt Disney, including "The African Lion."
  • D. Marie Allison
    Marie Allison was the wife of legendary American jazz drummer and bandleader Buddy Rich.
  • E. Marie Morgan
    Marie Morgan is a central female character in Ernest Hemingway’s novel and its film adaptation "To Have and Have Not," often depicted as a tough, resourceful, and romantically involved companion to the protagonist.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889df6dc081908f67dbadc03c07ee completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e454626cfc8190a2602ba4934b8e6d completed April 19, 2026, 4:04 a.m.
Created at: April 10, 2026, 5:49 a.m.