Triple

T2202563
Position Surface form Disambiguated ID Type / Status
Subject Parliament of Kazakhstan E50523 entity
Predicate location P40 FINISHED
Object Astana E50521 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: Astana | Statement: [Parliament of Kazakhstan, location, Astana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Astana
Context triple: [Parliament of Kazakhstan, location, Astana]
  • A. Astana chosen
    Astana is the planned, modernist capital city of Kazakhstan, known for its futuristic architecture and rapid development since the late 20th century.
  • B. Almaty
    Almaty is the largest city and main commercial and cultural center of Kazakhstan, located in the country’s mountainous southeast.
  • C. Ürümqi
    Ürümqi is a major city in northwestern China that serves as the political, economic, and cultural center of the Xinjiang Uyghur Autonomous Region.
  • D. Karaganda
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • E. Shymkent
    Shymkent is one of the largest and most populous cities in southern Kazakhstan, serving as a key industrial, commercial, and cultural center of the region.
  • 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_69a88b044ab48190add007487680f009 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abbfa33f0881908403604eafb73ecf completed March 7, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3b515c081909d6ad7f0506ea5a8 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:46 p.m.