Triple

T20456457
Position Surface form Disambiguated ID Type / Status
Subject Frederick Hollander E501799 entity
Predicate hasWorkLocation P1527 FINISHED
Object UFA studios 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: UFA studios | Statement: [Frederick Hollander, hasWorkLocation, UFA studios]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UFA studios
Context triple: [Frederick Hollander, hasWorkLocation, UFA studios]
  • A. UFA film studios
    UFA film studios was a major German film production company that became a central force in shaping the innovative and influential cinema of the Weimar Republic.
  • B. UFA
    UFA is the acronym for the Uniformed Firefighters Association, the labor union representing New York City’s rank-and-file firefighters.
  • C. UFA chosen
    UFA (Universum Film AG) was a major German film production and distribution company, especially prominent during the Weimar Republic and early 20th-century cinema.
  • D. Gorky Film Studio
    Gorky Film Studio is a major Soviet and Russian film studio, historically known for producing children’s films and notable cinematic works in Moscow.
  • E. Mosfilm
    Mosfilm is one of Russia’s largest and oldest film studios, renowned for producing many of the Soviet Union’s most iconic movies.
  • 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_69e0b4ad4940819098cf2ff6413574e5 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e696a1b03c8190984d9db6d3251308 completed April 20, 2026, 9:12 p.m.
Created at: April 16, 2026, 11:32 a.m.