Triple
T9928475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dong-gu, Busan |
E192582
|
entity |
| Predicate | hasKoreanName |
P17869
|
FINISHED |
| Object |
동구
동구는 대한민국 부산광역시에 위치한 자치구로, 항만과 원도심 일대를 포함하는 행정구역이다.
|
E829190
|
NE FINISHED |
How this triple was built (4 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: 동구 | Statement: [Dong-gu, Busan, hasKoreanName, 동구]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 동구 Context triple: [Dong-gu, Busan, hasKoreanName, 동구]
-
A.
북구
북구 is a Korean administrative district name commonly used for "Buk-gu" (North District) in various cities across South Korea.
-
B.
남구
남구는 대한민국 여러 도시에서 도심 남쪽에 위치한 행정구역(구)으로, 북구와 대칭되는 지리적·행정적 구분을 나타내는 명칭이다.
-
C.
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.
-
D.
Suyeong-gu
Suyeong-gu is a coastal district in the city of Busan, South Korea, known for its urban neighborhoods and proximity to popular beaches.
-
E.
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 동구 Triple: [Dong-gu, Busan, hasKoreanName, 동구]
Generated description
동구는 대한민국 부산광역시에 위치한 자치구로, 항만과 원도심 일대를 포함하는 행정구역이다.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 동구 Target entity description: 동구는 대한민국 부산광역시에 위치한 자치구로, 항만과 원도심 일대를 포함하는 행정구역이다.
-
A.
북구
북구 is a Korean administrative district name commonly used for "Buk-gu" (North District) in various cities across South Korea.
-
B.
남구
남구는 대한민국 여러 도시에서 도심 남쪽에 위치한 행정구역(구)으로, 북구와 대칭되는 지리적·행정적 구분을 나타내는 명칭이다.
-
C.
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.
-
D.
Suyeong-gu
Suyeong-gu is a coastal district in the city of Busan, South Korea, known for its urban neighborhoods and proximity to popular beaches.
-
E.
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
- F. None of above. chosen
Provenance (5 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_69d20e1eace88190a591cbab02153869 |
completed | April 5, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69d20ed2dea481909fd9a9dddac3daf1 |
completed | April 5, 2026, 7:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d20fef723881909d8d57548461926f |
completed | April 5, 2026, 7:31 a.m. |
Created at: March 30, 2026, 8:43 p.m.