Triple
T20537097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yongsan-gu |
E504227
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Seongdong-gu |
—
|
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: Seongdong-gu | Statement: [Yongsan-gu, borderedBy, Seongdong-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seongdong-gu Context triple: [Yongsan-gu, borderedBy, Seongdong-gu]
-
A.
Seongdong District
chosen
Seongdong District is an urban ward in eastern central Seoul, South Korea, known for its mix of residential neighborhoods, commercial areas, and redevelopment zones along the Han River.
-
B.
Seongbuk-gu
Seongbuk-gu is a district in northern Seoul, South Korea, known for its residential neighborhoods, cultural sites, and several major universities.
-
C.
Yongsan-gu
Yongsan-gu is a central district of Seoul, South Korea, known for its diverse neighborhoods, major transportation hubs, and significant commercial and cultural centers.
-
D.
Eunpyeong-gu
Eunpyeong-gu is a district in northwestern Seoul, South Korea, known for its mix of urban residential areas and access to nearby mountains and temples.
-
E.
Gangseo-gu
Gangseo-gu is a western coastal district of Busan, South Korea, known for its industrial complexes, logistics hubs, and proximity to Gimhae International Airport.
- 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_69e0b4b476648190bc6019622ae54d3c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a28f2f1081908f656d790ff182ff |
completed | April 20, 2026, 10:02 p.m. |
Created at: April 16, 2026, 11:37 a.m.