Triple

T3777945
Position Surface form Disambiguated ID Type / Status
Subject Oksana Baiul E83352 entity
Predicate givenName P17 FINISHED
Object Oksana
Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
 E389917 NE FINISHED

How this triple was built (4 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 | Statement: [Oksana Baiul, givenName, Oksana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Oksana
Context triple: [Oksana Baiul, givenName, Oksana]
  • A. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • B. Oksana Markarova
    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.
  • C. Xenia Shestova
    Xenia Shestova was a Russian noblewoman and influential matriarch of the early Romanov dynasty, best known as the mother of Tsar Mikhail I of Russia.
  • D. Daria Kulik
    Daria Kulik is the daughter of Russian Olympic figure skating champion Ilia Kulik.
  • E. Tatjana
    Tatjana is a feminine given name, commonly used in various European countries as a variant of Tatyana.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Oksana
Triple: [Oksana Baiul, givenName, Oksana]
Generated description
Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Oksana
Target entity description: Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
  • A. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • B. Oksana Markarova
    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.
  • C. Xenia Shestova
    Xenia Shestova was a Russian noblewoman and influential matriarch of the early Romanov dynasty, best known as the mother of Tsar Mikhail I of Russia.
  • D. Daria Kulik
    Daria Kulik is the daughter of Russian Olympic figure skating champion Ilia Kulik.
  • E. Tatjana
    Tatjana is a feminine given name, commonly used in various European countries as a variant of Tatyana.
  • F. None of above. chosen

Provenance (5 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc5d3dbc8190b6ab118a56acd5a3 completed March 8, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb1616008190a6ec9a2df5bac2b4 completed March 14, 2026, 6:07 a.m.
NEDg Description generation batch_69b4fc463bf48190b604d478f2c3e477 completed March 14, 2026, 6:12 a.m.
NED2 Entity disambiguation (via description) batch_69b4fcb59c148190b2b574c787473896 completed March 14, 2026, 6:14 a.m.
Created at: March 8, 2026, 3:36 p.m.