Triple

T13324903
Position Surface form Disambiguated ID Type / Status
Subject Wu Sangui E317414 entity
Predicate controlledRegion P15936 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: [Wu Sangui, controlledRegion, Guizhou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guizhou
Context triple: [Wu Sangui, controlledRegion, 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. Bié Province
    Bié Province is a central Angolan province known for its highland terrain, agricultural activity, and strategic location bordering several other provinces.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9992c1fec8190bcb6a6bb3c973a24 completed April 11, 2026, 12:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7546fbd1881909d2dda1edb63df2f completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:30 p.m.