Triple

T23266113
Position Surface form Disambiguated ID Type / Status
Subject Akmal Ikramov E588150 entity
Predicate regionOfActivity P82 FINISHED
Object Uzbekistan NE NERFINISHED

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: Uzbekistan | Statement: [Akmal Ikramov, regionOfActivity, Uzbekistan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uzbekistan
Context triple: [Akmal Ikramov, regionOfActivity, Uzbekistan]
  • A. Uzbekistan chosen
    Uzbekistan is a landlocked Central Asian country known for its Silk Road heritage, including historic cities like Samarkand and Bukhara, and for being a major producer of cotton and natural gas.
  • B. Tajikistan
    Tajikistan is a landlocked, mountainous country in Central Asia known for its rugged Pamir range and as a former Soviet republic with an economy centered on agriculture, hydropower, and remittances.
  • C. Turkmenistan
    Turkmenistan is a landlocked Central Asian country rich in natural gas resources, known for its desert landscapes, authoritarian political system, and capital city Ashgabat.
  • D. Takestan
    Takestan is a city in northwestern Iran known as an important agricultural and viticultural center within Qazvin Province.
  • E. Özbek
    Özbek is a Turkish surname borne by various individuals, including figures in business, sports, and public life.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d148adc819088efbf42672604e9 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f194cd13b48190a9c282545a34f348 completed April 29, 2026, 5:19 a.m.
Created at: April 17, 2026, 4:36 p.m.