Triple

T7946018
Position Surface form Disambiguated ID Type / Status
Subject Miao languages E184499 entity
Predicate spokenIn P2266 FINISHED
Object Guizhou E57456 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: Guizhou | Statement: [Miao languages, spokenIn, Guizhou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guizhou
Context triple: [Miao languages, spokenIn, Guizhou]
  • A. Guizhou Province chosen
    Guizhou Province is a mountainous, ethnically diverse region in southwest China known for its karst landscapes, cool climate, and rapid economic development.
  • B. Yunnan Province
    Yunnan Province is a mountainous, ethnically diverse region in southwest China known for its rich biodiversity, tea culture, and border location with countries such as Myanmar, Laos, and Vietnam.
  • C. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • D. Guangxi Province
    Guangxi Province is an autonomous region in southern China known for its ethnically diverse population, karst landscapes, and strategic location bordering Vietnam.
  • E. Sichuan Province
    Sichuan Province is a populous landlocked region in southwestern China known for its spicy cuisine, rich cultural heritage, and diverse mountainous landscapes including parts of the Tibetan Plateau.
  • 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_69ca8291c2008190b1b8832c87814bcf completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b29a570819091a2ac185a8d57c4 completed March 31, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd33cf48188190b57ac2fb1dbfa771 completed April 1, 2026, 3:03 p.m.
Created at: March 30, 2026, 5:09 p.m.