Triple

T15645136
Position Surface form Disambiguated ID Type / Status
Subject Hanzhong Street E376158 entity
Predicate district P2709 FINISHED
Object Wanhua District NE NERFINISHED

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: Wanhua District | Statement: [Hanzhong Street, district, Wanhua District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wanhua District
Context triple: [Hanzhong Street, district, Wanhua District]
  • A. Wanhua District chosen
    Wanhua District is one of Taipei’s oldest urban areas, known for its historic temples, traditional markets, and the popular shopping and entertainment area of Ximending.
  • B. Chengzhong District
    Chengzhong District is a central urban district of Xining, the capital city of Qinghai Province in northwest China.
  • C. Tianxin District
    Tianxin District is a central urban district of Changsha, the capital city of Hunan Province in China, known for its historical sites and commercial areas.
  • D. Wanghua District
    Wanghua District is an urban district of the city of Fushun in Liaoning Province, northeastern China, known for its role in the region’s industrial and residential development.
  • E. Jianhua District
    Jianhua District is a central urban district of Qiqihar City in Heilongjiang Province, northeastern China.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d85cd1564c8190991adda63bfab4b0 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ed400ec8190a14a9f7cf3092865 completed April 16, 2026, 2:52 a.m.
Created at: April 10, 2026, 4:15 a.m.