Triple

T16154697
Position Surface form Disambiguated ID Type / Status
Subject Ruifang District E392007 entity
Predicate borderedBy P224 FINISHED
Object Xizhi District NE NERFINISHED

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: Xizhi District | Statement: [Ruifang District, borderedBy, Xizhi District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xizhi District
Context triple: [Ruifang District, borderedBy, Xizhi District]
  • A. Xizhi District chosen
    Xizhi District is a suburban district of New Taipei City in northern Taiwan, known for its residential communities, technology parks, and convenient access to central Taipei.
  • B. Yinhai District
    Yinhai District is an urban district of Beihai City in Guangxi, China, known for its coastal location and role in the city's economic and administrative activities.
  • C. Yu’an District
    Yu’an District is an urban administrative district under the jurisdiction of Lu’an City in Anhui Province, China.
  • D. Guishan District
    Guishan District is an urban district in Taoyuan City, Taiwan, known for its mix of residential areas, industrial zones, and educational institutions.
  • E. Zhongzheng District
    Zhongzheng District is a central administrative and commercial district of Taipei, Taiwan, known for housing key government institutions, major transportation hubs, and important cultural landmarks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21e5902a08190ad8694955ef6073a completed April 17, 2026, 11:49 a.m.
Created at: April 10, 2026, 5:01 a.m.