Triple

T3644417
Position Surface form Disambiguated ID Type / Status
Subject Wuchang District E77262 entity
Predicate borders P224 FINISHED
Object Hongshan District E69519 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: Hongshan District | Statement: [Wuchang District, borders, Hongshan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hongshan District
Context triple: [Wuchang District, borders, Hongshan District]
  • A. Hongshan District chosen
    Hongshan District is an urban district of Wuhan in Hubei Province, China, known for its educational institutions, technology parks, and major transportation hubs.
  • B. 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.
  • C. Tieshan District
    Tieshan District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China, known for its industrial and mining activities.
  • D. Jinyuan District
    Jinyuan District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
  • E. Qingshan District
    Qingshan District is an urban district of Wuhan in Hubei Province, China, known for its heavy industry and riverside location along the Yangtze River.
  • 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_69ad85de1b988190a45f8dbfebc806fc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc35c28908190b253f4835918a2b4 completed March 8, 2026, 6:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69badb12b7488190bcd0be87b6278876 completed March 18, 2026, 5:04 p.m.
Created at: March 8, 2026, 3:24 p.m.