Triple

T10669533
Position Surface form Disambiguated ID Type / Status
Subject Dongdan E251449 entity
Predicate administrativeDivision P747 FINISHED
Object Dongdan Subdistrict E251449 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: Dongdan Subdistrict | Statement: [Dongdan, administrativeDivision, Dongdan Subdistrict]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dongdan Subdistrict
Context triple: [Dongdan, administrativeDivision, Dongdan Subdistrict]
  • A. Dongdan chosen
    Dongdan is a central commercial and transportation hub in Beijing known for its shopping streets, offices, and busy intersections.
  • B. Dong Da District
    Dong Da District is a central urban district of Hanoi, Vietnam, known for its dense residential areas, historical sites, and role as a major commercial and educational hub of the capital.
  • C. Dong District
    Dong District is an administrative district (gu) of Incheon, a major port city in northwestern South Korea.
  • D. Dongbao District
    Dongbao District is an urban administrative district under the jurisdiction of Jingmen City in Hubei Province, China, serving as the city's central area.
  • E. Pyongchon District
    Pyongchon District is a central urban district of Pyongyang, North Korea, known for its industrial facilities and major national institutions.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6f861513881909b44c711371086b7 completed April 9, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69d98865f700819093c8cadc6fcef75f completed April 10, 2026, 11:31 p.m.
Created at: April 8, 2026, 9:09 p.m.