Triple

T16360370
Position Surface form Disambiguated ID Type / Status
Subject Incheon E397293 entity
Predicate hasDistrict P459 FINISHED
Object Seo District E159654 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: Seo District | Statement: [Incheon, hasDistrict, Seo District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seo District
Context triple: [Incheon, hasDistrict, Seo District]
  • A. Seo District chosen
    Seo District is a western coastal district of Incheon, South Korea, known for its industrial complexes, port facilities, and growing residential areas.
  • B. Daowai District
    Daowai District is an urban district of Harbin in Heilongjiang Province, China, known for its historic architecture and traditional neighborhoods.
  • C. Xi District
    Xi District is an urban administrative district of the city of Panzhihua in Sichuan Province, China.
  • D. Shapingba District
    Shapingba District is a major urban district of Chongqing, China, known for its universities, historical sites, and role as an educational and cultural center of the city.
  • E. Hongo district
    Hongo district is a historic and academic neighborhood in Tokyo’s Bunkyō ward, known for institutions like the University of Tokyo and its traditional residential character.
  • 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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2fad241848190a9f32c7b050f20a5 completed April 18, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01673979608190905afae3071413c0 completed May 11, 2026, 5:20 a.m.
Created at: April 10, 2026, 5:08 a.m.