Triple

T18933652
Position Surface form Disambiguated ID Type / Status
Subject Yu’an District E463182 entity
Predicate partOf P40 FINISHED
Object Lu’an City 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: Lu’an City | Statement: [Yu’an District, partOf, Lu’an City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lu’an City
Context triple: [Yu’an District, partOf, Lu’an City]
  • A. Lu’an chosen
    Lu’an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain, tea production, and historical sites.
  • B. Yao City
    Yao City is a municipality in Osaka Prefecture, Japan, known as a residential and industrial suburb within the Osaka metropolitan area.
  • C. Sanhe City
    Sanhe City is a county-level city in Hebei Province, China, located near Beijing and forming part of the Beijing–Tianjin–Hebei metropolitan region.
  • D. Wu’an
    Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
  • E. Yuncheng
    Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
  • 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_69d8dcfec90481909e926be9767e5779 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d3e57e648190aa4d3b09e84d4d38 completed April 20, 2026, 7:21 a.m.
Created at: April 10, 2026, 11:59 a.m.