Triple

T12438034
Position Surface form Disambiguated ID Type / Status
Subject Tatiana Nikolayeva E297198 entity
Predicate familyName P18 FINISHED
Object Nikolayeva
Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
 E1000684 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: Nikolayeva | Statement: [Tatiana Nikolayeva, familyName, Nikolayeva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nikolayeva
Context triple: [Tatiana Nikolayeva, familyName, Nikolayeva]
  • A. Tarasova
    Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
  • B. Volkova
    Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
  • C. Paveletskaya
    Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
  • D. Yukhnov
    Yukhnov is a small historic town in western Russia known for its location on the Ugra River and its role in regional trade and World War II history.
  • E. Voykovskaya
    Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
  • 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: Nikolayeva
Triple: [Tatiana Nikolayeva, familyName, Nikolayeva]
Generated description
Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nikolayeva
Target entity description: Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
  • A. Tarasova
    Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
  • B. Volkova
    Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
  • C. Paveletskaya
    Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
  • D. Yukhnov
    Yukhnov is a small historic town in western Russia known for its location on the Ugra River and its role in regional trade and World War II history.
  • E. Voykovskaya
    Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
  • 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_69d6ada166c48190b902972cd2408fa3 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d8dc0f881908a3da736d8947ce1 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c66449081909484b945b6b97643 completed May 2, 2026, 10:36 p.m.
NEDg Description generation batch_69f67ebf53e08190beffaef7903adbd4 completed May 2, 2026, 10:46 p.m.
NED2 Entity disambiguation (via description) batch_69f67f3993b481909b5de1c9873945a2 completed May 2, 2026, 10:48 p.m.
Created at: April 8, 2026, 9:55 p.m.