Triple
T19117323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ui LRT |
E467938
|
entity |
| Predicate | locale |
P387
|
FINISHED |
| Object | Seongbuk District |
E348269
|
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: Seongbuk District | Statement: [Ui LRT, locale, Seongbuk District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seongbuk District Context triple: [Ui LRT, locale, Seongbuk District]
-
A.
Seongbuk District
chosen
Seongbuk District is a residential and educational borough in northern Seoul, South Korea, known for its universities, cultural sites, and traditional neighborhoods.
-
B.
Gwangjin District
Gwangjin District is an eastern borough of Seoul, South Korea, known for its universities, shopping areas, and location along the Han River.
-
C.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
D.
Gangseo District
Gangseo District is a western coastal district of Busan, South Korea, known for its industrial complexes, logistics hubs, and proximity to Gimhae International Airport.
-
E.
Gangseo District
Gangseo District is a western administrative district of Seoul, South Korea, known for its residential areas, transportation hubs, and proximity to Gimpo International Airport.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e399a6d8819090a9501ff1637b9d |
completed | April 20, 2026, 8:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07d4212b448190952cbd0e9772f115 |
completed | May 16, 2026, 2:19 a.m. |
Created at: April 10, 2026, 12:05 p.m.