Triple

T4910344
Position Surface form Disambiguated ID Type / Status
Subject Belogorsk E110214 entity
Predicate governingBody P46 FINISHED
Object Belogorsk city administration E110214 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: Belogorsk city administration | Statement: [Belogorsk, governingBody, Belogorsk city administration]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belogorsk city administration
Context triple: [Belogorsk, governingBody, Belogorsk city administration]
  • A. Belogorsk chosen
    Belogorsk is a city in Russia’s Far East that serves as an important regional center within Amur Oblast.
  • B. city of Gavrilov-Yam
    The city of Gavrilov-Yam is a small industrial and cultural center in central Russia, known historically for its textile and yarn production.
  • C. city of Poshekhonye
    The city of Poshekhonye is a small historic town in central Russia known for its traditional cheese production and location along the Sogozha River.
  • D. city of Vologda
    The city of Vologda is a historic regional center in northwestern Russia, known for its well-preserved wooden architecture, Orthodox monasteries, and traditional lace-making.
  • E. Arkhangelsk City Administration
    Arkhangelsk City Administration is the municipal government responsible for managing local affairs, public services, and development policies in the city of Arkhangelsk, Russia.
  • 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_69bd44132b94819088522d92beaadc78 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e9acc0c819080b07e2e7924d163 completed March 20, 2026, 3:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fe43a888190ab1b150da0f49203 completed March 21, 2026, 10:16 a.m.
Created at: March 20, 2026, 1:29 p.m.