Triple

T9482875
Position Surface form Disambiguated ID Type / Status
Subject Fuling District E228686 entity
Predicate locatedOn P40 FINISHED
Object Wu River E99764 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: Wu River | Statement: [Fuling District, locatedOn, Wu River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wu River
Context triple: [Fuling District, locatedOn, Wu River]
  • A. Wu River chosen
    The Wu River is a significant river in southwestern China known for flowing through deep gorges and contributing substantially to the Yangtze River system.
  • B. Jialing River
    The Jialing River is a significant river in southwestern China that flows through Sichuan and Chongqing, contributing heavily to the region’s water resources, transportation, and ecology.
  • C. Hunjiang River
    The Hunjiang River is a significant river in northeastern China that serves as a major tributary within the Yalu River basin.
  • D. Luo River
    The Luo River is a significant tributary in central China that flows through Henan and Shaanxi provinces before joining the Yellow River.
  • E. Jingjiang River
    Jingjiang River is a historically significant, highly sinuous section of the Yangtze River in central China, known for its sharp bends and extensive river-training works.
  • 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd804c859081908c261ad16b501f0d completed April 1, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d32a3c9c6881908757c63a0ccdc54e completed April 6, 2026, 3:36 a.m.
Created at: March 30, 2026, 7:55 p.m.