Triple
T9087714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seo District, Busan |
E217800
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
Seo-gu
Seo-gu is a coastal district in the southwestern part of Busan, South Korea, known for its port facilities, historic sites, and urban residential areas.
|
E823067
|
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: Seo-gu | Statement: [Seo District, Busan, shortName, Seo-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seo-gu Context triple: [Seo District, Busan, shortName, Seo-gu]
-
A.
Seo-gu
Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
-
B.
Seo-gu
Seo-gu is an administrative district in the city of Daegu, South Korea, known primarily as a residential and commercial urban area.
-
C.
Sasang-gu
Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
-
D.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
E.
Sŏch'o-gu
Sŏch'o-gu is the McCune–Reischauer romanization of Seocho District, a major administrative and residential area in southern Seoul, South Korea.
- 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: Seo-gu Triple: [Seo District, Busan, shortName, Seo-gu]
Generated description
Seo-gu is a coastal district in the southwestern part of Busan, South Korea, known for its port facilities, historic sites, and urban residential areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seo-gu Target entity description: Seo-gu is a coastal district in the southwestern part of Busan, South Korea, known for its port facilities, historic sites, and urban residential areas.
-
A.
Seo-gu
Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
-
B.
Seo-gu
Seo-gu is an administrative district in the city of Daegu, South Korea, known primarily as a residential and commercial urban area.
-
C.
Sasang-gu
Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
-
D.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
E.
Sŏch'o-gu
Sŏch'o-gu is the McCune–Reischauer romanization of Seocho District, a major administrative and residential area in southern Seoul, South Korea.
- 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc965646408190b041ab0e2d5dbc94 |
completed | April 1, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc2ca7a081908f597e9a58920d6e |
completed | April 5, 2026, 2:42 a.m. |
| NEDg | Description generation | batch_69d1ccb043008190a3af47b234520891 |
completed | April 5, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1cd04694881909ab19cb4c11fbd20 |
completed | April 5, 2026, 2:46 a.m. |
Created at: March 30, 2026, 7:13 p.m.