Triple
T22309567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hongdae |
E551476
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Mapo District |
—
|
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: Mapo District | Statement: [Hongdae, locatedIn, Mapo District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mapo District Context triple: [Hongdae, locatedIn, Mapo District]
-
A.
Mapo District
chosen
Mapo District is a vibrant administrative and cultural area in western Seoul, South Korea, known for neighborhoods like Hongdae and its lively arts, nightlife, and dining scenes.
-
B.
Omate District
Omate District is an administrative district located within Peru's southern Andean region, known for its rural communities and highland landscapes.
-
C.
Getasan District
Getasan District is an administrative district in Central Java, Indonesia, located within Semarang Regency and known for its rural highland landscapes near Mount Merbabu.
-
D.
Nangang District
Nangang District is a central urban district of Harbin, China, known as a major administrative, commercial, and educational hub of the city.
-
E.
Nangang District
Nangang District is an eastern district of Taipei, Taiwan, known for its technology parks, transportation hubs, and role as a growing center for business and innovation.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574d53148190a1ec07f849e1ae9d |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:42 p.m.