Triple

T12324700
Position Surface form Disambiguated ID Type / Status
Subject Nikita Anisimov E293797 entity
Predicate basedIn P40 FINISHED
Object Moscow, Russia E1747 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: Moscow, Russia | Statement: [Nikita Anisimov, basedIn, Moscow, Russia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moscow, Russia
Context triple: [Nikita Anisimov, basedIn, Moscow, Russia]
  • A. Moscow
    Moscow is a fictional character from the Spanish television series "Money Heist" (La Casa de Papel), known as a kind-hearted, blue-collar miner and the father of Denver who participates in the Royal Mint heist.
  • B. Moscow chosen
    Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
  • C. Moscow
    Moscow is a small borough in Lackawanna County, Pennsylvania, known as a residential community near the Scranton metropolitan area.
  • D. Moscow City
    Moscow City is a modern high-rise business district in western Moscow known for its cluster of skyscrapers, financial institutions, and commercial developments.
  • E. Mosca
    Mosca is the cunning and manipulative servant in Ben Jonson’s play "Volpone," known for orchestrating deceptions and driving much of the plot’s dark comedy.
  • 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4e7e588190b37e2413bc649198 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65e9e909081909b341398e7aae954 completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 9:53 p.m.