Triple

T15776544
Position Surface form Disambiguated ID Type / Status
Subject Tashkent Metro E382505 entity
Predicate hasStation P35 FINISHED
Object Toshkent station E382504 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 station | Statement: [Tashkent Metro, hasStation, Toshkent station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toshkent station
Context triple: [Tashkent Metro, hasStation, Toshkent station]
  • A. Tashkent railway station chosen
    Tashkent railway station is the main rail hub of Uzbekistan’s capital, serving as a key junction for domestic and international train services across Central Asia.
  • B. Chorsu station
    Chorsu station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan, serving the historic Chorsu Bazaar area.
  • C. Alisher Navoiy station
    Alisher Navoiy station is a Tashkent Metro station named after the famed Uzbek poet Alisher Navoi and noted for its distinctive architectural and artistic design.
  • D. Beruniy station
    Beruniy station is a metro station in Tashkent, Uzbekistan, serving as part of the city's Tashkent Metro rapid transit system.
  • E. Amir Temur Xiyoboni station
    Amir Temur Xiyoboni station is a metro station in Tashkent, Uzbekistan, named after the historic figure Amir Timur and serving as part of the city's rapid transit network.
  • 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_69ffa12b98a48190acc6f6566ef13f94 completed May 9, 2026, 9:03 p.m.
Created at: April 10, 2026, 4:47 a.m.