Triple

T9549360
Position Surface form Disambiguated ID Type / Status
Subject Karen Shakhnazarov E230378 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: [Karen Shakhnazarov, employer, Mosfilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mosfilm
Context triple: [Karen Shakhnazarov, 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. Gorky Film Studio
    Gorky Film Studio is a major Soviet and Russian film studio, historically known for producing children’s films and notable cinematic works in Moscow.
  • 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. Lenfilm
    Lenfilm is one of Russia’s oldest and most prominent film studios, based in Saint Petersburg and known for producing many classic Soviet-era movies.
  • E. Lenfilm Studios
    Lenfilm Studios is one of Russia’s oldest and most prominent film studios, historically based in Saint Petersburg and known for producing many classic Soviet-era films.
  • 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_69cd99059138819088ae54b26df979cf completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d22840c4548190b1610e2c3cec6220 completed April 5, 2026, 9:15 a.m.
Created at: March 30, 2026, 8:02 p.m.