Triple

T4526896
Position Surface form Disambiguated ID Type / Status
Subject Konya Province E106200 entity
Predicate hasDistrict P459 FINISHED
Object Taşkent
Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
 E449763 NE FINISHED

How this triple was built (4 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: Taşkent | Statement: [Konya Province, hasDistrict, Taşkent]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Taşkent
Context triple: [Konya Province, hasDistrict, Taşkent]
  • A. 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.
  • B. Nukus
    Nukus is the capital of the autonomous Republic of Karakalpakstan in western Uzbekistan, known for its remote desert location and the renowned Nukus Museum of Art.
  • C. Yoshkar-Ola
    Yoshkar-Ola is a city in central Russia that serves as the administrative, cultural, and economic center of the Mari El Republic.
  • D. Alma-Atinskaya
    Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
  • E. Karshi
    Karshi is a city in southern Uzbekistan known as an important regional center for industry, agriculture, and transportation.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Taşkent
Triple: [Konya Province, hasDistrict, Taşkent]
Generated description
Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Taşkent
Target entity description: Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
  • A. 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.
  • B. Nukus
    Nukus is the capital of the autonomous Republic of Karakalpakstan in western Uzbekistan, known for its remote desert location and the renowned Nukus Museum of Art.
  • C. Yoshkar-Ola
    Yoshkar-Ola is a city in central Russia that serves as the administrative, cultural, and economic center of the Mari El Republic.
  • D. Alma-Atinskaya
    Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
  • E. Karshi
    Karshi is a city in southern Uzbekistan known as an important regional center for industry, agriculture, and transportation.
  • F. None of above. chosen

Provenance (5 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57760f4481908f69ce82be63d7f8 completed March 20, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda4577d0c8190a88cdf4523446329 completed March 20, 2026, 7:47 p.m.
NEDg Description generation batch_69bda8367b988190bd6859581ba9a38e completed March 20, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69bda8b4d064819083de58ee18458fa8 completed March 20, 2026, 8:06 p.m.
Created at: March 20, 2026, 1:03 p.m.