Triple
T224223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busan |
E4279
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
|
E33247
|
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: Suyeong District | Statement: [Busan, hasDistrict, Suyeong District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suyeong District Context triple: [Busan, hasDistrict, Suyeong District]
-
A.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
B.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
C.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
-
D.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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: Suyeong District Triple: [Busan, hasDistrict, Suyeong District]
Generated description
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suyeong District Target entity description: Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
A.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
B.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
C.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
-
D.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c7194fc8190a2d02d446ae3a75e |
completed | Feb. 28, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a37952cbbc8190afd920510408fd31 |
completed | Feb. 28, 2026, 11:25 p.m. |
| NEDg | Description generation | batch_69a379ec65a48190a379e35cc0867ac2 |
completed | Feb. 28, 2026, 11:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a37a81393881908a2a6c345d6f89fa |
completed | Feb. 28, 2026, 11:30 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.