Triple

T11172218
Position Surface form Disambiguated ID Type / Status
Subject Bless the Woman E264301 entity
Predicate productionCompany P490 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: [Bless the Woman, productionCompany, Mosfilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mosfilm
Context triple: [Bless the Woman, productionCompany, 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_69d6aa9dafac8190bd90d2c74f661aa7 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e89660208190b1d9e91529f5d246 completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e463b155a08190b361b38a39d25b1f completed April 19, 2026, 5:10 a.m.
Created at: April 8, 2026, 9:29 p.m.