Triple

T15238114
Position Surface form Disambiguated ID Type / Status
Subject Xinyi District E364181 entity
Predicate contains P35 FINISHED
Object Xinyi Planning District E364181 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: Xinyi Planning District | Statement: [Xinyi District, contains, Xinyi Planning District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xinyi Planning District
Context triple: [Xinyi District, contains, Xinyi Planning District]
  • A. Xinyi District chosen
    Xinyi District is a modern commercial and financial hub of Taipei, Taiwan, known for its skyscrapers, luxury shopping, and vibrant nightlife.
  • B. Pingxi District
    Pingxi District is a rural, mountainous area in New Taipei City, Taiwan, famed for its historic coal-mining villages, scenic railway, and annual sky lantern festival.
  • C. Xinyi
    Xinyi is a county-level city administered by Xuzhou in Jiangsu Province, eastern China.
  • D. Xinyi
    Xinyi is a county-level city administered by Maoming in Guangdong Province, China, known for its agriculture and regional commerce.
  • E. 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.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007da7e988190925a9b67b8070bc7 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d3b3fc0819094daf892200bd1ac completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 3:12 a.m.