Triple

T8271274
Position Surface form Disambiguated ID Type / Status
Subject Kulik E193432 entity
Predicate hasNotableBearer P458 FINISHED
Object Yelena Kulik
Yelena Kulik is a Russian former competitive figure skater known for her performances in ladies' singles during the 1990s.
 E736968 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: Yelena Kulik | Statement: [Kulik, hasNotableBearer, Yelena Kulik]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yelena Kulik
Context triple: [Kulik, hasNotableBearer, Yelena Kulik]
  • A. Irina Kulik
    Irina Kulik is a Russian art critic, journalist, and lecturer known for her work on contemporary art and culture.
  • B. Elena Kulik
    Elena Kulik is a Russian figure skater known for competing internationally in the 1990s.
  • C. Tatiana Kulik
    Tatiana Kulik is a person notable enough to be recognized as a bearer of the surname Kulik, though specific widely known public details about her are not clearly established.
  • D. 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.
  • E. Tatiana Tarasova
    Tatiana Tarasova is a renowned Russian figure skating coach and choreographer known for guiding numerous skaters to Olympic and World Championship titles.
  • 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: Yelena Kulik
Triple: [Kulik, hasNotableBearer, Yelena Kulik]
Generated description
Yelena Kulik is a Russian former competitive figure skater known for her performances in ladies' singles during the 1990s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yelena Kulik
Target entity description: Yelena Kulik is a Russian former competitive figure skater known for her performances in ladies' singles during the 1990s.
  • A. Irina Kulik
    Irina Kulik is a Russian art critic, journalist, and lecturer known for her work on contemporary art and culture.
  • B. Elena Kulik chosen
    Elena Kulik is a Russian figure skater known for competing internationally in the 1990s.
  • C. Tatiana Kulik
    Tatiana Kulik is a person notable enough to be recognized as a bearer of the surname Kulik, though specific widely known public details about her are not clearly established.
  • D. 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.
  • E. Tatiana Tarasova
    Tatiana Tarasova is a renowned Russian figure skating coach and choreographer known for guiding numerous skaters to Olympic and World Championship titles.
  • F. None of above.

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_69ca82e14ae481908ffdb822cd2192bc completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7986f8cc8190a529dda980dd6e98 completed March 31, 2026, 7:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69cea7f7060c81908ad70682063c66f1 completed April 2, 2026, 5:31 p.m.
NEDg Description generation batch_69cea994f0ac819092fb34a0f2357611 completed April 2, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69ceaa4c7ba08190be86cccc3a857656 completed April 2, 2026, 5:41 p.m.
Created at: March 30, 2026, 5:50 p.m.