Triple

T19934441
Position Surface form Disambiguated ID Type / Status
Subject Markarova E479137 entity
Predicate hasNotableBearer P458 FINISHED
Object Oksana Markarova NE NERFINISHED

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: Oksana Markarova | Statement: [Markarova, hasNotableBearer, Oksana Markarova]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oksana Markarova
Context triple: [Markarova, hasNotableBearer, Oksana Markarova]
  • A. Oksana Markarova chosen
    Oksana Markarova is a Ukrainian economist and politician who served as Ukraine’s Minister of Finance and later became the country’s ambassador to the United States.
  • B. Oksana Yatskaya
    Oksana Yatskaya is a Belarusian long-distance runner known for competing internationally in events such as the marathon.
  • C. Oksana Yarygina
    Oksana Yarygina is a female given name bearer notable enough to be specifically referenced, likely for achievements in a professional or public field.
  • D. Oksana Shmachkova
    Oksana Shmachkova is a person known for bearing the given name Oksana, though specific widely recognized public achievements or roles are not well documented.
  • E. Oksana Skidanova
    Oksana Skidanova is a notable individual recognized as a prominent bearer of the given name Oksana.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e522a17c819095165d4d24939fd8 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65a161a6c819084165ea528ec2f64 completed April 20, 2026, 4:53 p.m.
Created at: April 10, 2026, 1:53 p.m.