Triple

T9549467
Position Surface form Disambiguated ID Type / Status
Subject Dziga Vertov E230380 entity
Predicate employer P7 FINISHED
Object Sovkino E210739 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: Sovkino | Statement: [Dziga Vertov, employer, Sovkino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sovkino
Context triple: [Dziga Vertov, employer, Sovkino]
  • A. Sovkino chosen
    Sovkino was a Soviet state film production and distribution company active in the 1920s, instrumental in developing early Soviet cinema and promoting revolutionary propaganda films.
  • B. Rubtsovsk
    Rubtsovsk is an industrial city in Altai Krai, Russia, known as the birthplace of Raisa Gorbacheva and for its role as a regional agricultural and machinery center.
  • C. Peredelkino
    Peredelkino is a writers’ village near Moscow, Russia, historically known as a retreat and residence for many prominent Soviet and Russian authors, including Boris Pasternak.
  • D. Pirogovo
    Pirogovo is a settlement located near the Pirogovskoye Reservoir, known as a local residential and recreational area.
  • E. Krasnopresnenskaya
    Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural design.
  • 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_69ca847d3be8819099c9dad2a7e786f1 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9906bc90819086f105c453e63c83 completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c82c98c8190a4fd6fc3ceb4173d completed April 4, 2026, 5:38 p.m.
Created at: March 30, 2026, 8:02 p.m.