Triple

T959340
Position Surface form Disambiguated ID Type / Status
Subject Operation Koltso E20698 entity
Predicate commander P1061 FINISHED
Object Andrey Yeremenko E10049 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: Andrey Yeremenko | Statement: [Operation Koltso, commander, Andrey Yeremenko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Andrey Yeremenko
Context triple: [Operation Koltso, commander, Andrey Yeremenko]
  • A. Andrey Yeremenko chosen
    Andrey Yeremenko was a Soviet general and Marshal of the Soviet Union who played a key leadership role on the Eastern Front during World War II, particularly in major operations against Nazi Germany.
  • B. Pavel Zhigarev
    Pavel Zhigarev was a prominent Soviet military aviator and Marshal of Aviation who served as a leading commander of the Soviet Air Forces during and after World War II.
  • C. Vyacheslav Kozlov
    Vyacheslav Kozlov is a Russian former professional ice hockey winger known for his scoring ability and long NHL career, including key roles with the Detroit Red Wings and Atlanta Thrashers.
  • D. Andrei Voronkov
    Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
  • E. Igor Babuschkin
    Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
  • 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_69a493b21f2881908132dcf45dcd2f36 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b412f9f48190be123e8c20f38962 completed March 1, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae5d73c10c8190a059c3ae6b3a6279 completed March 9, 2026, 5:41 a.m.
Created at: March 1, 2026, 7:40 p.m.