Triple

T4066907
Position Surface form Disambiguated ID Type / Status
Subject President of Tajikistan E86345 entity
Predicate seat P75 FINISHED
Object Dushanbe E98543 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: Dushanbe | Statement: [President of Tajikistan, seat, Dushanbe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dushanbe
Context triple: [President of Tajikistan, seat, Dushanbe]
  • A. Dushanbe chosen
    Dushanbe is the largest city and political, economic, and cultural center of Tajikistan.
  • B. Tokmok
    Tokmok is a small city in northern Kyrgyzstan, near the border with Kazakhstan, known as an industrial and agricultural center in the Chüy Valley.
  • 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. Tashkurgan
    Tashkurgan is a remote town in China’s Xinjiang region, inhabited mainly by Tajik people and known as a key stop on the Karakoram Highway near the Pakistan border.
  • E. Bishkek
    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.
  • 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_69aed93c69208190a4efac0efe3cd69b completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbf73a1c81909f1741f4ecf55f98 completed March 9, 2026, 4:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69b562b17c888190ac4771f2bb4f0d58 completed March 14, 2026, 1:29 p.m.
Created at: March 9, 2026, 3:38 p.m.