Triple

T8573099
Position Surface form Disambiguated ID Type / Status
Subject Bayannur E202975 entity
Predicate hasSubdivision P747 FINISHED
Object Linhe District E763473 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: Linhe District | Statement: [Bayannur, hasSubdivision, Linhe District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Linhe District
Context triple: [Bayannur, hasSubdivision, Linhe District]
  • A. Linhe District chosen
    Linhe District is the urban administrative center of Bayannur in Inner Mongolia, China, serving as its political and economic hub.
  • B. Hecheng District
    Hecheng District is the central urban district and administrative seat of Huaihua in Hunan Province, China.
  • C. Dongsheng District
    Dongsheng District is the central urban district and administrative hub of Ordos City in Inner Mongolia, China.
  • D. Wanbailin District
    Wanbailin District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
  • E. Qilihe District
    Qilihe District is an urban administrative district of Lanzhou, the capital city of Gansu Province in northwestern China.
  • 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_69ca8328ebe481909a8c038fa79959b4 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea458c1081908e79bee2cbf97207 completed March 31, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69cffd4c307881909996adedc959180f completed April 3, 2026, 5:47 p.m.
Created at: March 30, 2026, 6:21 p.m.