Triple

T17306618
Position Surface form Disambiguated ID Type / Status
Subject Vladimir Makarov E420179 entity
Predicate hasAlias P455 FINISHED
Object Makarov E436841 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: Makarov | Statement: [Vladimir Makarov, hasAlias, Makarov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Makarov
Context triple: [Vladimir Makarov, hasAlias, Makarov]
  • A. Makarov chosen
    Makarov is a Russian surname most prominently associated with notable figures such as Hall of Fame ice hockey player Sergei Makarov.
  • B. Fedor Tokarev
    Fedor Tokarev was a prominent Soviet firearms designer best known for creating influential weapons such as the Tokarev pistol and the SVT-40 semi-automatic rifle.
  • C. Fedorov
    Fedorov is a common Russian surname borne by numerous notable figures in fields such as sports, science, and the arts.
  • D. Vassili Zaitsev
    Vassili Zaitsev is a legendary Soviet sniper of World War II, renowned for his exploits during the Battle of Stalingrad and later popularized in film and literature.
  • E. Pavlichenko
    Pavlichenko is a Ukrainian surname most famously borne by Lyudmila Pavlichenko, a celebrated Soviet World War II sniper.
  • 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_69d889d22b848190a4663d0b8f8f76e7 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e438ff3ee08190ab4c44a22f86b38b completed April 19, 2026, 2:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0180e0c1b881908aa2b6b4d8ac04b6 completed May 11, 2026, 7:10 a.m.
Created at: April 10, 2026, 5:43 a.m.