Triple

T9063662
Position Surface form Disambiguated ID Type / Status
Subject Sergei Parajanov E217190 entity
Predicate educatedAt P5 FINISHED
Object VGIK E40646 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: VGIK | Statement: [Sergei Parajanov, educatedAt, VGIK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VGIK
Context triple: [Sergei Parajanov, educatedAt, VGIK]
  • A. VGIK chosen
    VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
  • B. VKSU
    VKSU is a public university located in Ara, Bihar, India, offering undergraduate and postgraduate programs across various disciplines.
  • C. VChK
    VChK is the Russian abbreviation for the Cheka, the Soviet Union’s first secret police and state security organization established after the 1917 Revolution.
  • D. VU
    VU is a major research university in Amsterdam, Netherlands, known for its wide range of academic programs and emphasis on interdisciplinary and socially engaged scholarship.
  • E. VIGR
    VIGR is the ICAO airport code assigned to Gwalior Airport in Gwalior, India.
  • 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_69ca83d5a7f48190b16c1e59bd43ede0 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc94b9f28481909e20366b0e3d14aa completed April 1, 2026, 3:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfebf8ecb48190b1802b5b41bc7aec completed April 3, 2026, 4:34 p.m.
Created at: March 30, 2026, 7:11 p.m.