Triple
T9928493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dong-gu, Busan |
E192582
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Daeshin-dong |
E692103
|
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: Daeshin-dong | Statement: [Dong-gu, Busan, contains, Daeshin-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daeshin-dong Context triple: [Dong-gu, Busan, contains, Daeshin-dong]
-
A.
Daechi-dong
Daechi-dong is a wealthy neighborhood in Seoul renowned for its dense concentration of private academies and highly competitive educational culture.
-
B.
Yeoksam-dong
Yeoksam-dong is a major commercial and residential neighborhood in Seoul, South Korea, known for its dense cluster of corporate offices, tech companies, and vibrant urban amenities.
-
C.
Yeonsu-dong
Yeonsu-dong is a neighborhood within Incheon, South Korea, known as a residential and local commercial area of Yeonsu District.
-
D.
Sogyeok-dong
Sogyeok-dong is a neighborhood in central Seoul, South Korea, known for its traditional Korean houses (hanok), art galleries, and proximity to historic palaces.
-
E.
Daeyeon-dong
chosen
Daeyeon-dong is a neighborhood in the southern part of Busan, South Korea, known for its residential areas, educational institutions, and proximity to the city's coastal attractions.
- 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_69ca82dd978c8190947124ab0d3315ac |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb59d7ad08190982a1584547190bd |
completed | April 2, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d979c21f5481908bea7fd2c70d2c0b |
completed | April 10, 2026, 10:29 p.m. |
Created at: March 30, 2026, 8:43 p.m.