Triple

T10480919
Position Surface form Disambiguated ID Type / Status
Subject Jilu dialect E247165 entity
Predicate region P40 FINISHED
Object Jilu E751913 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: Jilu | Statement: [Jilu dialect, region, Jilu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jilu
Context triple: [Jilu dialect, region, Jilu]
  • A. Hebei
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • B. Guanghe
    Guanghe was an era name used during the reign of Emperor Ling of the Eastern Han dynasty in ancient China.
  • C. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • D. Yuezhou
    Yuezhou is the historical name of the city now known as Yueyang, an important cultural and transport hub in Hunan Province, China.
  • E. Zhili province chosen
    Zhili province was a historically important administrative region in northern China, centered on present-day Hebei and Beijing, that played a key political and military role during the late Qing and early Republican eras.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5095c5dc88190902582db28df01b4 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dc73991881909aa538fce1e05a7c completed April 10, 2026, 11:18 a.m.
Created at: April 6, 2026, 12:22 p.m.