Triple

T9049995
Position Surface form Disambiguated ID Type / Status
Subject Binh Duong Province E216857 entity
Predicate borders P224 FINISHED
Object Tay Ninh Province E216870 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: Tay Ninh Province | Statement: [Binh Duong Province, borders, Tay Ninh Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tay Ninh Province
Context triple: [Binh Duong Province, borders, Tay Ninh Province]
  • A. Tây Ninh Province chosen
    Tây Ninh Province is a region in southern Vietnam known as the spiritual center and birthplace of the syncretic Cao Dai religion.
  • B. Quang Duc Province
    Quang Duc Province was a former administrative province of South Vietnam located in the Central Highlands region.
  • C. Dak Lak Province
    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.
  • D. Binh Phuoc Province
    Binh Phuoc Province is a largely rural province in southern Vietnam known for its rubber plantations, cashew production, and location along the Cambodian border.
  • E. 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.
  • 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_69ca83d362e88190ae44b4e4dc194209 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc6b52cc1881909fb011d9a8af2e18 completed April 1, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05beac7a08190a4e7634bf02ce304 completed April 4, 2026, 12:31 a.m.
Created at: March 30, 2026, 7:10 p.m.