Triple

T383158
Position Surface form Disambiguated ID Type / Status
Subject Catherine E8723 entity
Predicate hasVariant P455 FINISHED
Object Katya
Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
 E61396 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: Katya | Statement: [Catherine, hasVariant, Katya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Katya
Context triple: [Catherine, hasVariant, Katya]
  • A. Anastasia Shubskaya
    Anastasia Shubskaya is a Russian model and film producer best known as the wife of NHL star Alex Ovechkin.
  • B. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • C. Celia Lovsky
    Celia Lovsky was an Austrian-American character actress known for her distinctive roles in mid-20th-century film and television, including a memorable appearance as T’Pau in the original Star Trek series.
  • D. Tatyana Ovechkina
    Tatyana Ovechkina is a former Soviet Olympic champion basketball player and the mother of NHL star Alex Ovechkin.
  • E. Irina Korina
    Irina Korina is the mother of late Russian-American actor Anton Yelchin.
  • 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: Katya
Triple: [Catherine, hasVariant, Katya]
Generated description
Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Katya
Target entity description: Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
  • A. Anastasia Shubskaya
    Anastasia Shubskaya is a Russian model and film producer best known as the wife of NHL star Alex Ovechkin.
  • B. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • C. Celia Lovsky
    Celia Lovsky was an Austrian-American character actress known for her distinctive roles in mid-20th-century film and television, including a memorable appearance as T’Pau in the original Star Trek series.
  • D. Tatyana Ovechkina
    Tatyana Ovechkina is a former Soviet Olympic champion basketball player and the mother of NHL star Alex Ovechkin.
  • E. Irina Korina
    Irina Korina is the mother of late Russian-American actor Anton Yelchin.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec40ff8c81909306eb2dfe1512af completed Feb. 28, 2026, 1:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a477785b848190a1a09d20d94ceed5 completed March 1, 2026, 5:29 p.m.
NEDg Description generation batch_69a47a2224cc81909a057c612d591bba completed March 1, 2026, 5:40 p.m.
NED2 Entity disambiguation (via description) batch_69a47ab148fc8190a93d7e3c838583c2 completed March 1, 2026, 5:43 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.