Triple

T437872
Position Surface form Disambiguated ID Type / Status
Subject Andrey Yeremenko E10049 entity
Predicate familyName P18 FINISHED
Object 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: Yeremenko | Statement: [Andrey Yeremenko, familyName, Yeremenko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yeremenko
Context triple: [Andrey Yeremenko, familyName, 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. 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.
  • C. Vladimir Kolpakchi
    Vladimir Kolpakchi was a Soviet military commander and general best known for leading Red Army formations during World War II, including in the Battle of Stalingrad.
  • D. Vladimir Yurzinov
    Vladimir Yurzinov is a prominent Russian ice hockey coach and former player, best known for his successful leadership of top Soviet and Russian clubs and contributions to the national team.
  • E. Andrei Voronkov
    Andrei Voronkov is a computer scientist known for his influential work in automated reasoning and theorem proving.
  • 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef26bb78819089b3b5dac0330619 completed Feb. 28, 2026, 1:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5dc8df9708190976967ad4e45a597 completed March 2, 2026, 6:53 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.