Triple

T22050243
Position Surface form Disambiguated ID Type / Status
Subject Nüshu E544863 entity
Predicate usedIn P98 FINISHED
Object Hunan 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: Hunan | Statement: [Nüshu, usedIn, Hunan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hunan
Context triple: [Nüshu, usedIn, Hunan]
  • A. Hunan Province chosen
    Hunan Province is a landlocked region in south-central China known for its strategic location, spicy cuisine, and role as a major battleground and revolutionary base in modern Chinese history.
  • B. Hunan hydrological region
    The Hunan hydrological region is a major water system in south-central China that encompasses the river networks and drainage basins of Hunan Province, including key tributaries of the Yangtze River.
  • C. Hubei Province
    Hubei Province is a landlocked region in central China known for its capital city Wuhan, major role in industry and transportation, and significant historical and cultural heritage.
  • D. Bié Province
    Bié Province is a central Angolan province known for its highland terrain, agricultural activity, and strategic location bordering several other provinces.
  • E. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • 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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f128323fb08190b9592fd08a96cba0 completed April 28, 2026, 9:35 p.m.
Created at: April 16, 2026, 8:26 p.m.