Triple

T7775441
Position Surface form Disambiguated ID Type / Status
Subject Dr. Mabuse the Gambler E221378 entity
Predicate distributor P1951 FINISHED
Object UFA E355973 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: UFA | Statement: [Dr. Mabuse the Gambler, distributor, UFA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UFA
Context triple: [Dr. Mabuse the Gambler, distributor, UFA]
  • A. 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.
  • 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 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.
  • D. UAFA
    UAFA is the Union of Arab Football Associations, the regional governing body that organizes football competitions among Arab countries in Asia and Africa.
  • E. UPA
    UPA is a major Indian political coalition led by the Indian National Congress that has formed the central government of India multiple times.
  • 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_69ca83ebbef881909ac47f789145fef7 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69caa4d005808190ac14c8d716421bdb completed March 30, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb59ca4c088190bc4f1f9b2488d996 completed March 31, 2026, 5:21 a.m.
Created at: March 30, 2026, 3:45 p.m.