Triple

T10860301
Position Surface form Disambiguated ID Type / Status
Subject Chenzhou E256382 entity
Predicate hasDistrict P459 FINISHED
Object Beihu District E922562 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: Beihu District | Statement: [Chenzhou, hasDistrict, Beihu District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beihu District
Context triple: [Chenzhou, hasDistrict, Beihu District]
  • A. Beihu District chosen
    Beihu District is an urban district that serves as the central administrative and commercial hub of Chenzhou in Hunan Province, China.
  • B. Baiji District
    Baiji District is an administrative district in Iraq known for encompassing the city of Baiji, a major oil refining and industrial center within Salah ad Din Governorate.
  • C. Shuangxi District
    Shuangxi District is a rural, mountainous district in eastern New Taipei City, Taiwan, known for its rivers, old streets, and natural scenery.
  • D. Hecheng District
    Hecheng District is the central urban district and administrative seat of Huaihua in Hunan Province, China.
  • E. Honggu District
    Honggu District is an administrative urban district of Lanzhou in Gansu Province, China, known for its role in the city's industrial and resource-based development.
  • 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_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75150ceb88190a70356d12ce130c5 completed April 9, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69e5b76fa8348190bb42f1c71eb0e545 completed April 20, 2026, 5:19 a.m.
Created at: April 8, 2026, 9:20 p.m.