Triple
T9433810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwangan-dong |
E227450
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Haeundae-gu |
E199270
|
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: Haeundae-gu | Statement: [Gwangan-dong, near, Haeundae-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haeundae-gu Context triple: [Gwangan-dong, near, Haeundae-gu]
-
A.
Haeundae District
chosen
Haeundae District is a coastal district of Busan, South Korea, famous for its popular beach, tourism, and cultural attractions.
-
B.
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.
-
C.
Dongnae District
Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
-
D.
Seocho District
Seocho District is a major affluent ward in southern Seoul, South Korea, known for its legal institutions, upscale residential areas, and proximity to the Gangnam business district.
-
E.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e62d53c81908055e0967e6cd54d |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d32a3c9c6881908757c63a0ccdc54e |
completed | April 6, 2026, 3:36 a.m. |
Created at: March 30, 2026, 7:49 p.m.