Triple

T2469049
Position Surface form Disambiguated ID Type / Status
Subject Kiev Special Military District E55324 entity
Predicate garrisonCity P10998 FINISHED
Object Kyiv E17733 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: Kyiv | Statement: [Kiev Special Military District, garrisonCity, Kyiv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyiv
Context triple: [Kiev Special Military District, garrisonCity, Kyiv]
  • A. Kyiv chosen
    Kyiv is the capital and largest city of Ukraine, serving as its political, cultural, and economic center.
  • B. Kharkiv
    Kharkiv is Ukraine’s second-largest city and a major industrial, cultural, and educational center in the northeast of the country.
  • C. Dnipro
    Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
  • D. Kremenchuk
    Kremenchuk is an industrial city in central Ukraine on the Dnieper River, historically significant as a major transport and strategic hub.
  • E. Chernihiv
    Chernihiv is a historic city in northern Ukraine known for its ancient churches, rich cultural heritage, and role as a regional administrative and memorial center.
  • 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_69ab49e3622c8190ad22afa2c4fbb807 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd134684c8190bc62c0af22d75538 completed March 7, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69b276b188248190b015eec2b83bc20b completed March 12, 2026, 8:17 a.m.
Created at: March 6, 2026, 9:44 p.m.