Triple

T21709741
Position Surface form Disambiguated ID Type / Status
Subject Gudermessky District E535869 entity
Predicate administrativeCenter P1474 FINISHED
Object Gudermes NE NERFINISHED

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: Gudermes | Statement: [Gudermessky District, administrativeCenter, Gudermes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gudermes
Context triple: [Gudermessky District, administrativeCenter, Gudermes]
  • A. Gudermes chosen
    Gudermes is a town in the Chechen Republic of Russia that serves as an important regional transport and administrative center.
  • B. Yamburg
    Yamburg is the former name of the Russian town now known as Kingisepp, located in Leningrad Oblast near the border with Estonia.
  • C. Karachayevsk
    Karachayevsk is a town in southwestern Russia located in the North Caucasus region, serving as one of the main urban centers of the Karachay-Cherkess Republic.
  • D. Kasimov
    Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
  • E. Kazanin
    Kazanin is a Russian-language surname most notably borne by comedian and television personality Stepan Kazanin.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46b44c0819088ab883ebd44e0e8 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efb5321d34819091f3cd03f7b407c0 completed April 27, 2026, 7:12 p.m.
Created at: April 16, 2026, 6:46 p.m.