Triple

T20389593
Position Surface form Disambiguated ID Type / Status
Subject Olivia Mazursky E498049 entity
Predicate familyName P18 FINISHED
Object Mazursky 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: Mazursky | Statement: [Olivia Mazursky, familyName, Mazursky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mazursky
Context triple: [Olivia Mazursky, familyName, Mazursky]
  • A. Mazursky chosen
    Mazursky is the surname of Paul Mazursky, an American film director, screenwriter, and actor known for his satirical and socially observant movies.
  • B. Lucian Maisel
    Lucian Maisel is an American actor best known for his role in the 2009 family drama film "Everybody’s Fine."
  • C. Garfinkle
    Garfinkle is the original surname of American actor John Garfield, a prominent film star of the 1930s and 1940s known for his intense, naturalistic performances.
  • D. Mr. Mushnik
    Mr. Mushnik is the gruff, profit-driven owner of the struggling flower shop in the horror-comedy musical "Little Shop of Horrors."
  • E. Yutz
    Yutz is a commune in northeastern France situated in the Moselle department, known for its residential character and proximity to the city of Thionville.
  • 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_69e0b4a71ebc8190b153a36c738730f4 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6790e65a081909832855758fffd14 completed April 20, 2026, 7:05 p.m.
Created at: April 16, 2026, 11:28 a.m.