Triple

T22482632
Position Surface form Disambiguated ID Type / Status
Subject Sichuan Science and Technology Museum E555803 entity
Predicate province P604 FINISHED
Object Sichuan 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: Sichuan | Statement: [Sichuan Science and Technology Museum, province, Sichuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sichuan
Context triple: [Sichuan Science and Technology Museum, province, Sichuan]
  • A. Sichuan Province chosen
    Sichuan Province is a populous landlocked region in southwestern China known for its spicy cuisine, rich cultural heritage, and diverse mountainous landscapes including parts of the Tibetan Plateau.
  • B. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • C. Sichuan Basin
    The Sichuan Basin is a large, fertile lowland region in southwestern China, surrounded by mountains and known as a major agricultural and population center.
  • D. Yang Province
    Yang Province was an ancient administrative region in southeastern China that encompassed parts of modern Jiangsu, Anhui, and surrounding areas during the Han and Three Kingdoms periods.
  • E. Wuchuan
    Wuchuan is a county-level city administered by Zhanjiang in Guangdong Province, southern China.
  • 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_69e11e53897c819088863779f8c50bb0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15c3a3b688190b41599979d038d85 completed April 29, 2026, 1:17 a.m.
Created at: April 16, 2026, 8:49 p.m.