Triple

T8053009
Position Surface form Disambiguated ID Type / Status
Subject Xinyu E187720 entity
Predicate hasDistrict P459 FINISHED
Object Yushui District E727253 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: Yushui District | Statement: [Xinyu, hasDistrict, Yushui District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yushui District
Context triple: [Xinyu, hasDistrict, Yushui District]
  • A. Yushui District chosen
    Yushui District is the central urban district and administrative seat of Xinyu, a prefecture-level city in Jiangxi Province, China.
  • B. Yuhua District
    Yuhua District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
  • C. Mawei District
    Mawei District is an urban district of Fuzhou in Fujian Province, China, historically known as a key shipbuilding and maritime center.
  • D. Yunxi District
    Yunxi District is an urban administrative district of Yueyang City in Hunan Province, China, known for its location along the Yangtze River and Dongting Lake region.
  • E. Xialu District
    Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, 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_69ca82b15e948190a62fd7af5218426a completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3f7c425c8190aa1b2f534afeb58c completed March 31, 2026, 3:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde6f2bf388190ad3849317659756e completed April 2, 2026, 3:48 a.m.
Created at: March 30, 2026, 5:25 p.m.