Triple

T8769143
Position Surface form Disambiguated ID Type / Status
Subject Grigori Aleksandrov E208411 entity
Predicate employer P7 FINISHED
Object Gorky Film Studio E801543 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: Gorky Film Studio | Statement: [Grigori Aleksandrov, employer, Gorky Film Studio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gorky Film Studio
Context triple: [Grigori Aleksandrov, employer, Gorky Film Studio]
  • A. Gorky Film Studio chosen
    Gorky Film Studio is a major Soviet and Russian film studio, historically known for producing children’s films and notable cinematic works in Moscow.
  • B. Mosfilm
    Mosfilm is one of Russia’s largest and oldest film studios, renowned for producing many of the Soviet Union’s most iconic movies.
  • 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. Goskino
    Goskino was the Soviet state film committee responsible for overseeing and producing motion pictures in the USSR, including landmark works of early Soviet cinema.
  • E. Gerasimov Institute of Cinematography
    The Gerasimov Institute of Cinematography is a renowned Russian film school in Moscow, considered one of the oldest and most prestigious institutions for cinema and television education in the world.
  • 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_69ca835edb4481909b4aafb616dc5eb7 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5eedc7188190a67d959b9af53837 completed March 31, 2026, 11:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69d16101372c8190bbd0bd3c2389298d completed April 4, 2026, 7:05 p.m.
Created at: March 30, 2026, 6:41 p.m.