Triple

T11570165
Position Surface form Disambiguated ID Type / Status
Subject Shaoshan City E274363 entity
Predicate province P604 FINISHED
Object Hunan E30061 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: Hunan | Statement: [Shaoshan City, province, Hunan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hunan
Context triple: [Shaoshan City, province, 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. 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.
  • C. Bié Province
    Bié Province is a central Angolan province known for its highland terrain, agricultural activity, and strategic location bordering several other provinces.
  • D. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • E. northwestern Hunan
    Northwestern Hunan is a subregion of China’s Hunan Province characterized by its inland location, river plains, and a mix of urban centers and agricultural areas.
  • 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_69d6aae5ac3c81908d2b0a3a665665b2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88dd543a48190b834abd8e8ae7b65 completed April 10, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ee869d0b0481908080e7f3a80223d1 completed April 26, 2026, 9:41 p.m.
Created at: April 8, 2026, 9:37 p.m.