Triple

T468063
Position Surface form Disambiguated ID Type / Status
Subject Hong Kong E8492 entity
Predicate borders P224 FINISHED
Object Shenzhen E18299 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: Shenzhen | Statement: [Hong Kong, borders, Shenzhen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shenzhen
Context triple: [Hong Kong, borders, Shenzhen]
  • A. Guangzhou
    Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
  • B. Zhuhai
    Zhuhai is a coastal city in Guangdong Province, China, known for its proximity to Macau, its role in the Pearl River Delta economic zone, and its reputation as a popular tourist destination.
  • C. Shenzhen, China chosen
    Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
  • D. 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.
  • E. Shenzhen Nanshan District
    Shenzhen Nanshan District is a major urban district of Shenzhen, China, known for its concentration of high-tech industries, universities, and innovation hubs such as Shenzhen High-Tech Industrial Park.
  • 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_69a2e7f3aeb48190a19453e3a043f486 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2efd9bea081909ee782840f3da12b completed Feb. 28, 2026, 1:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69a533bc40ac81908426b70366259a3b completed March 2, 2026, 6:52 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.