Triple

T9549468
Position Surface form Disambiguated ID Type / Status
Subject Dziga Vertov E230380 entity
Predicate employer P7 FINISHED
Object Vostokkino 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: Vostokkino | Statement: [Dziga Vertov, employer, Vostokkino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vostokkino
Context triple: [Dziga Vertov, employer, Vostokkino]
  • A. 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.
  • B. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • C. 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.
  • D. Profsoyuznaya station
    Profsoyuznaya station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
  • E. Yelets
    Yelets is a historic city in western Russia known for its medieval origins, traditional lace-making, and role as a regional cultural and economic center.
  • 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.