Triple

T16305889
Position Surface form Disambiguated ID Type / Status
Subject Tanggula Mountains E395908 entity
Predicate TibetanName P744 FINISHED
Object Dangla E1206813 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: Dangla | Statement: [Tanggula Mountains, TibetanName, Dangla]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dangla
Context triple: [Tanggula Mountains, TibetanName, Dangla]
  • A. Dangla chosen
    Dangla is another name for the Tanglha mountain range, a high-altitude range in the central Tibetan Plateau known for its rugged peaks and significant role in regional climate and hydrology.
  • B. Delingha
    Delingha is a county-level city in northwestern China that serves as an important administrative and transportation hub in Qinghai Province.
  • C. Dhala
    Dhala is a town and district in southwestern Yemen, historically part of the British-era protectorate structures that later formed the Federation of South Arabia.
  • D. Dargai
    Dargai is a town in Pakistan’s Khyber Pakhtunkhwa province, historically known as a strategic and military site in the Malakand region.
  • E. Amdanga
    Amdanga is a town in the North 24 Parganas district of the Indian state of West Bengal, known primarily as a semi-rural locality within the Kolkata metropolitan 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_69d87f23bb088190a16fbb91a1957ea5 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e288d5619081909d0f8157cc487877 completed April 17, 2026, 7:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a002da444408190b770055d84060f4d completed May 10, 2026, 7:03 a.m.
Created at: April 10, 2026, 5:06 a.m.