Triple

T16126361
Position Surface form Disambiguated ID Type / Status
Subject Cabinet of Ministers of Kyrgyzstan E391281 entity
Predicate seat P75 FINISHED
Object Bishkek E94665 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: Bishkek | Statement: [Cabinet of Ministers of Kyrgyzstan, seat, Bishkek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bishkek
Context triple: [Cabinet of Ministers of Kyrgyzstan, seat, Bishkek]
  • A. Bishkek chosen
    Bishkek is the largest city and political, economic, and cultural center of Kyrgyzstan, located in the north of the country near the Kyrgyz Ala-Too mountain range.
  • B. Kokshetau
    Kokshetau is a city in northern Kazakhstan that serves as the administrative and economic center of the surrounding Akmola Region.
  • C. Almaty
    Almaty is the largest city and main commercial and cultural center of Kazakhstan, located in the country’s mountainous southeast.
  • D. Tashkent
    Tashkent is the capital and largest city of Uzbekistan, a major cultural and economic hub in Central Asia with deep historical ties to the Islamic world.
  • E. Karaganda
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • 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_69d87f1bb0988190b490d273dbf3fd03 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e20204fb408190b58d49d0d64bb740 completed April 17, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69fffeef277481908ab35bbff06c827a completed May 10, 2026, 3:43 a.m.
Created at: April 10, 2026, 5 a.m.