Triple

T13492658
Position Surface form Disambiguated ID Type / Status
Subject Soviet 3rd Army E320673 entity
Predicate notableCommander P1197 FINISHED
Object Yakov Kreizer E849798 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: Yakov Kreizer | Statement: [Soviet 3rd Army, notableCommander, Yakov Kreizer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yakov Kreizer
Context triple: [Soviet 3rd Army, notableCommander, Yakov Kreizer]
  • A. Yakov Rechter
    Yakov Rechter was a prominent Israeli architect known for his modernist public buildings and cultural institutions, which helped shape the architectural landscape of Israel in the 20th century.
  • B. Yefim Kopelyan
    Yefim Kopelyan was a prominent Soviet theater and film actor, renowned for his distinctive voice and extensive work as a narrator in Soviet cinema and television.
  • C. Moshe Kuninsky
    Moshe Kuninsky is an Israeli politician who serves as the mayor of the city of Karmiel.
  • D. Stepan Chernyak chosen
    Stepan Chernyak was a Soviet military commander best known for leading Red Army forces during World War II.
  • E. Issur Danielovitch
    Issur Danielovitch, better known as Kirk Douglas, was a legendary American actor and producer renowned for his intense performances in classic films such as "Spartacus."
  • 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_69d807629d6c8190998f1b9bb12d2ed0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaf4c66008190b287e0551889d7c8 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7a835ea448190a5ddaf8479e0b36c completed May 3, 2026, 7:55 p.m.
Created at: April 9, 2026, 9:43 p.m.