Triple

T7968281
Position Surface form Disambiguated ID Type / Status
Subject Tanka people E185260 entity
Predicate region P40 FINISHED
Object Guangxi E99799 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: Guangxi | Statement: [Tanka people, region, Guangxi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guangxi
Context triple: [Tanka people, region, Guangxi]
  • A. Guangxi Province chosen
    Guangxi Province is an autonomous region in southern China known for its ethnically diverse population, karst landscapes, and strategic location bordering Vietnam.
  • B. Xiangkhouang Province
    Xiangkhouang Province is a mountainous region in northeastern Laos known for its war history, cool climate, and the mysterious megalithic Plain of Jars archaeological landscape.
  • C. Guangdong Province
    Guangdong Province is a populous and economically vital coastal region in southern China, known for major cities like Guangzhou and Shenzhen and its role as a manufacturing and trade hub.
  • D. Guizhou Province
    Guizhou Province is a mountainous, ethnically diverse region in southwest China known for its karst landscapes, cool climate, and rapid economic development.
  • E. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • 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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bd06ee081908c5080003fb7b8f7 completed March 31, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce8828faf48190927b2a6680f6b4d8 completed April 2, 2026, 3:15 p.m.
Created at: March 30, 2026, 5:13 p.m.