Triple

T2920995
Position Surface form Disambiguated ID Type / Status
Subject Kow-Ata underground lake E78722 entity
Predicate locatedNear P294 FINISHED
Object Ashgabat E78716 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: Ashgabat | Statement: [Kow-Ata underground lake, locatedNear, Ashgabat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ashgabat
Context triple: [Kow-Ata underground lake, locatedNear, Ashgabat]
  • A. Ashgabat chosen
    Ashgabat is the largest city and political, economic, and cultural center of Turkmenistan, known for its grand marble architecture and monumental cityscape.
  • B. Baku
    Baku is the capital and largest city of Azerbaijan, known for its rich blend of Islamic heritage and modern architecture on the shores of the Caspian Sea.
  • C. 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.
  • D. Atyrau
    Atyrau is a city in western Kazakhstan located near the Caspian Sea, notable for straddling the boundary between Europe and Asia and serving as a major center for the country’s oil industry.
  • E. Karaganda
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a672f88190851e487dac18d43f completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc632afc819098021312c748346b completed March 11, 2026, 5:23 a.m.
Created at: March 8, 2026, 2:54 p.m.