Triple

T233660
Position Surface form Disambiguated ID Type / Status
Subject Chiang Kai-shek E4461 entity
Predicate deathPlace P21 FINISHED
Object Taipei, Taiwan E14412 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: Taipei, Taiwan | Statement: [Chiang Kai-shek, deathPlace, Taipei, Taiwan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taipei, Taiwan
Context triple: [Chiang Kai-shek, deathPlace, Taipei, Taiwan]
  • A. Taipei, Taiwan chosen
    Taipei, Taiwan is the capital and largest city of Taiwan, known as a major political, economic, and cultural center in East Asia.
  • B. Shanghai
    Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
  • C. Shenzhen, China
    Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
  • D. Hong Kong, China
    Hong Kong, China is a major global financial and trading hub and a Special Administrative Region of China located on the southern coast of the country.
  • E. Beijing
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
  • 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_69a257363ffc81909757bde7ab3404da completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25cb07b5c8190a482154819912abf completed Feb. 28, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3673257f081908bcb84cbedef3c07 completed Feb. 28, 2026, 10:07 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.