Triple

T15776522
Position Surface form Disambiguated ID Type / Status
Subject Tashkent Metro E382505 entity
Predicate operator P179 FINISHED
Object Toshkent Metropoliteni E382505 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: Toshkent Metropoliteni | Statement: [Tashkent Metro, operator, Toshkent Metropoliteni]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toshkent Metropoliteni
Context triple: [Tashkent Metro, operator, Toshkent Metropoliteni]
  • A. Tashkent Metro chosen
    Tashkent Metro is the rapid transit system serving Uzbekistan’s capital, notable for its Soviet-era architecture and ornately decorated underground stations.
  • B. Taşkent
    Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
  • C. 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.
  • D. Ürümqi Metro
    Ürümqi Metro is the rapid transit system serving Ürümqi, the capital of China’s Xinjiang Uyghur Autonomous Region.
  • E. Navoi
    Navoi is an industrial city in central Uzbekistan known for its mining, metallurgy, and chemical industries.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e05199cd8881909462462cec34d35a completed April 16, 2026, 3:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff909b467c819097ee87f51d2001da completed May 9, 2026, 7:52 p.m.
Created at: April 10, 2026, 4:47 a.m.