Triple

T15340862
Position Surface form Disambiguated ID Type / Status
Subject Vasiliy Lomachenko E366789 entity
Predicate trainer P41095 FINISHED
Object Anatoly Lomachenko E1156014 NE FINISHED

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: Anatoly Lomachenko | Statement: [Vasiliy Lomachenko, trainer, Anatoly Lomachenko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anatoly Lomachenko
Context triple: [Vasiliy Lomachenko, trainer, Anatoly Lomachenko]
  • A. Anatoly Lomachenko chosen
    Anatoly Lomachenko is a renowned Ukrainian boxing trainer best known for developing the unique, highly technical style of his son, multi-division world champion Vasiliy Lomachenko.
  • B. Vasiliy Lomachenko
    Vasiliy Lomachenko is a Ukrainian professional boxer renowned for his exceptional footwork, technical skill, and multiple world titles across several weight divisions.
  • C. Yordenis Ugás
    Yordenis Ugás is a Cuban professional boxer and former WBA welterweight world champion known for his technical skill and notable victories over top contenders.
  • D. Oleksandr Gvozdyk
    Oleksandr Gvozdyk is a Ukrainian professional boxer and former world light heavyweight champion known for his technical skill and powerful punching.
  • E. Vlad Kliatchko
    Vlad Kliatchko is a senior technology and product executive known for leading core engineering and platform initiatives at Bloomberg L.P.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e12eb7c8190944a260aa1aa9156 completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff219635b08190a19dcaeb72240379 completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:17 a.m.