Triple

T10386513
Position Surface form Disambiguated ID Type / Status
Subject Tatars E244776 entity
Predicate populationCenter P2106 FINISHED
Object Moscow 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 | Statement: [Tatars, populationCenter, Moscow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moscow
Context triple: [Tatars, populationCenter, Moscow]
  • A. Moscow chosen
    Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
  • B. 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.
  • C. 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.
  • 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. Pushkino
    Pushkino is a town in Russia that serves as a suburban residential and industrial center northeast of Moscow.
  • 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_69d381b5116081908d85227bab6d3c0c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9a4e6748190bd9dd319de94c659 completed April 7, 2026, 11:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7953ba52881908d6d7e5b099c12d2 completed April 9, 2026, 12:02 p.m.
Created at: April 6, 2026, 12:05 p.m.