Triple

T13087520
Position Surface form Disambiguated ID Type / Status
Subject North District E310374 entity
Predicate locatedNear P294 FINISHED
Object Shenzhen River E134481 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 River | Statement: [North District, locatedNear, Shenzhen River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shenzhen River
Context triple: [North District, locatedNear, Shenzhen River]
  • A. Shenzhen River chosen
    The Shenzhen River is a boundary river in southern China that forms part of the border between Hong Kong and the city of Shenzhen.
  • B. Huangpu River
    The Huangpu River is a significant waterway in eastern China that flows through the heart of Shanghai, dividing the city and serving as a vital shipping and cultural artery.
  • C. Yuan River
    The Yuan River is a major waterway in south-central China that flows through several provinces before joining the Yangtze River system.
  • D. Danshui River
    Danshui River is a major river in northern Taiwan that flows through Taipei and empties into the Taiwan Strait, known for its scenic waterfronts and historical significance.
  • E. Dongjiang
    Dongjiang, also known as the Dong River, is a major river in southern China that serves as an important water source for cities including Hong Kong and Guangzhou.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d981378dd08190b4f00e4e5df0e480 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7265eba0481908417d2bb905874e5 completed May 3, 2026, 10:41 a.m.
Created at: April 9, 2026, 9:02 p.m.