Triple

T3136197
Position Surface form Disambiguated ID Type / Status
Subject Vera Glagoleva E65534 entity
Predicate employer P7 FINISHED
Object Mosfilm E40647 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: Mosfilm | Statement: [Vera Glagoleva, employer, Mosfilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mosfilm
Context triple: [Vera Glagoleva, employer, Mosfilm]
  • A. Mosfilm chosen
    Mosfilm is one of Russia’s largest and oldest film studios, renowned for producing many of the Soviet Union’s most iconic movies.
  • B. 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.
  • C. 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.
  • D. Constantin Film
    Constantin Film is a German film production and distribution company known for producing a wide range of international films, including major genre franchises.
  • E. CinéArts
    CinéArts is a Cinemark-owned brand of upscale movie theaters that focuses on presenting independent, foreign, and art-house films in a premium cinema environment.
  • 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_69ad8581c25c8190b0d85ba9b9baa531 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada564eacc8190a54d07b4eb31c196 completed March 8, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f8793488190aa31040edaf1d627 completed March 12, 2026, 12:57 a.m.
Created at: March 8, 2026, 3:05 p.m.