Triple

T20798964
Position Surface form Disambiguated ID Type / Status
Subject Mnong people E511987 entity
Predicate distribution P1356 FINISHED
Object Dak Lak province 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: Dak Lak province | Statement: [Mnong people, distribution, Dak Lak province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dak Lak province
Context triple: [Mnong people, distribution, Dak Lak province]
  • A. Dak Lak Province chosen
    Dak Lak Province is a mountainous region in Vietnam’s Central Highlands known for its coffee plantations, diverse ethnic communities, and the provincial capital Buon Ma Thuot.
  • B. Bac Kan Province
    Bac Kan Province is a mountainous, sparsely populated province in northeastern Vietnam known for its forests, ethnic minority communities, and scenic Ba Bể Lake.
  • C. Lam Dong Province
    Lam Dong Province is a mountainous region in Vietnam’s Central Highlands known for its cool climate, pine forests, and the popular tourist city of Da Lat.
  • D. Quang Duc Province
    Quang Duc Province was a former administrative province of South Vietnam located in the Central Highlands region.
  • E. Ben Tre Province
    Ben Tre Province is a coastal province in Vietnam’s Mekong Delta region, known for its extensive coconut plantations and intricate network of rivers and canals.
  • 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2aef2e08190a779ed1d516ecba7 completed April 21, 2026, 12:19 a.m.
Created at: April 16, 2026, 12:39 p.m.