Triple

T3637466
Position Surface form Disambiguated ID Type / Status
Subject New Taipei City E77106 entity
Predicate contains P35 FINISHED
Object Xindian River E78493 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: Xindian River | Statement: [New Taipei City, contains, Xindian River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xindian River
Context triple: [New Taipei City, contains, Xindian River]
  • A. Guandu River
    The Guandu River is a crucial waterway in Brazil that serves as the primary source of drinking water for much of the metropolitan region of Rio de Janeiro.
  • B. Tamsui River chosen
    The Tamsui River is a major river in northern Taiwan that flows through the Taipei metropolitan area before emptying into the Taiwan Strait.
  • C. Xiaoqing River
    The Xiaoqing River is a significant river in Shandong Province, China, flowing through the city of Jinan and serving as an important regional waterway.
  • D. Shinfa River
    The Shinfa River is a tributary watercourse that feeds into the Atbara River in northeastern Africa.
  • E. Liu River
    The Liu River is a significant river in the Guangxi Zhuang Autonomous Region of southern China, known for flowing through the industrial city of Liuzhou and contributing to the region’s transportation and economy.
  • 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_69ad85dd0be48190b738990cb20c4731 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc328e5e481909d26318c743bc84a completed March 8, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b488320c58819088f8cc677f675ec3 completed March 13, 2026, 9:57 p.m.
Created at: March 8, 2026, 3:24 p.m.