Triple

T15722895
Position Surface form Disambiguated ID Type / Status
Subject Uglich E381144 entity
Predicate administrativeCenterOf P383 FINISHED
Object Uglichsky District 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: Uglichsky District | Statement: [Uglich, administrativeCenterOf, Uglichsky District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uglichsky District
Context triple: [Uglich, administrativeCenterOf, Uglichsky District]
  • A. Uglichsky District chosen
    Uglichsky District is an administrative district in Yaroslavl Oblast, Russia, centered around the historic town of Uglich.
  • B. Urzhumsky District
    Urzhumsky District is an administrative and municipal district in Kirov Oblast, Russia, centered around the town of Urzhum and comprising surrounding rural territories.
  • C. Butyrsky District
    Butyrsky District is a residential and historically industrial administrative district in the north of Moscow, Russia.
  • D. Kirillovsky District
    Kirillovsky District is an administrative district in Vologda Oblast, Russia, known for its historic towns, monasteries, and northern Russian cultural heritage.
  • E. Gagarinsky District
    Gagarinsky District is an administrative district in Moscow, Russia, known for its residential areas, educational institutions, and major transport routes.
  • 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_69d86d9bf930819082b30cf6d169297c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fb1fdd4819088f3e243263e5f73 completed April 16, 2026, 2:55 a.m.
Created at: April 10, 2026, 4:45 a.m.