Triple

T15248425
Position Surface form Disambiguated ID Type / Status
Subject Հովհաննես Բաղրամյան E364447 entity
Predicate deathPlace P21 FINISHED
Object Մոսկվա, ԽՍՀՄ E984134 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: Մոսկվա, ԽՍՀՄ | Statement: [Հովհաննես Բաղրամյան, deathPlace, Մոսկվա, ԽՍՀՄ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Մոսկվա, ԽՍՀՄ
Context triple: [Հովհաննես Բաղրամյան, deathPlace, Մոսկվա, ԽՍՀՄ]
  • A. Moscow, Soviet Union chosen
    Moscow, Soviet Union was the capital and largest city of the Soviet Union, serving as its political, economic, and cultural center.
  • B. Moscow
    Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
  • C. 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.
  • D. Moscow
    Moscow is a small borough in Lackawanna County, Pennsylvania, known as a residential community near the Scranton metropolitan area.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f62b9c8190b9ad40e2d1912b63 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd491cd881908bad9660af9b6b8f completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:13 a.m.