Triple

T6322564
Position Surface form Disambiguated ID Type / Status
Subject Qinhuangdao E141777 entity
Predicate hasSubdivision P747 FINISHED
Object Haigang District E585189 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: Haigang District | Statement: [Qinhuangdao, hasSubdivision, Haigang District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haigang District
Context triple: [Qinhuangdao, hasSubdivision, Haigang District]
  • A. Haigang District chosen
    Haigang District is the central urban district and administrative, economic, and cultural hub of Qinhuangdao in Hebei Province, China.
  • B. Tieshangang District
    Tieshangang District is an administrative district of the coastal city of Beihai in Guangxi, China, known for its port and industrial activities.
  • C. Quanshan District
    Quanshan District is an urban administrative district of Xuzhou in Jiangsu Province, China, known as one of the city’s central built-up areas.
  • D. Taishan District
    Taishan District is an urban district of New Taipei City in northern Taiwan, known for its residential communities and educational institutions.
  • E. Lubei District
    Lubei District is an urban administrative district of the city of Tangshan in Hebei Province, China, known for its role in the region’s industrial and commercial activities.
  • 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_69c008d201748190917e69c41ba3f978 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c064e38f1c81909c7e90b520602bae completed March 22, 2026, 9:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6040b1e0481908095decce40107b4 completed March 27, 2026, 4:14 a.m.
Created at: March 22, 2026, 4:29 p.m.