Triple
T1360991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buk District |
E29097
|
entity |
| Predicate | hasNativeName |
P1435
|
FINISHED |
| Object |
북구
북구 is a Korean administrative district name commonly used for "Buk-gu" (North District) in various cities across South Korea.
|
E201807
|
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: 북구 | Statement: [Buk District, hasNativeName, 북구]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 북구 Context triple: [Buk District, hasNativeName, 북구]
-
A.
Namdong District
Namdong District is a major administrative and commercial hub of Incheon, South Korea, known for housing the city hall and various industrial and residential areas.
-
B.
Bupyeong District
Bupyeong District is a populous urban district of Incheon, South Korea, known as a major residential, commercial, and transportation hub in the metropolitan area.
-
C.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
D.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
E.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
- 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: 북구 Triple: [Buk District, hasNativeName, 북구]
Generated description
북구 is a Korean administrative district name commonly used for "Buk-gu" (North District) in various cities across South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 북구 Target entity description: 북구 is a Korean administrative district name commonly used for "Buk-gu" (North District) in various cities across South Korea.
-
A.
Namdong District
Namdong District is a major administrative and commercial hub of Incheon, South Korea, known for housing the city hall and various industrial and residential areas.
-
B.
Bupyeong District
Bupyeong District is a populous urban district of Incheon, South Korea, known as a major residential, commercial, and transportation hub in the metropolitan area.
-
C.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
D.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
E.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b156b081909c99ada70a969fc0 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5af961c8190aed3129dab0fecf3 |
completed | March 8, 2026, 5:45 p.m. |
| NEDg | Description generation | batch_69adb8b2b01c8190997179cdfd55da13 |
completed | March 8, 2026, 5:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adb94aaf348190a28ca8e9d9cacf41 |
completed | March 8, 2026, 6 p.m. |
Created at: March 1, 2026, 7:56 p.m.