Triple

T13187234
Position Surface form Disambiguated ID Type / Status
Subject Trans-Caspian Railway E313887 entity
Predicate endPoint P390 FINISHED
Object Tashkent E81695 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: Tashkent | Statement: [Trans-Caspian Railway, endPoint, Tashkent]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tashkent
Context triple: [Trans-Caspian Railway, endPoint, Tashkent]
  • A. Tashkent chosen
    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. Taşkent
    Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
  • C. 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.
  • D. Navoi
    Navoi is an industrial city in central Uzbekistan known for its mining, metallurgy, and chemical industries.
  • E. Yoshkar-Ola
    Yoshkar-Ola is a city in central Russia that serves as the administrative, cultural, and economic center of the Mari El Republic.
  • 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_69d806ae1e08819090d95bfe1538cc17 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c4b663c8190b0b18f0785f7b57d completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f716c1080c81908057f92f320a855b completed May 3, 2026, 9:34 a.m.
Created at: April 9, 2026, 9:15 p.m.