Triple
T20051553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seongdong District |
E499210
|
entity |
| Predicate | containsNeighborhood |
P4813
|
FINISHED |
| Object |
Doseon-dong
Doseon-dong is a neighborhood located within Seongdong District in Seoul, South Korea.
|
E1511372
|
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: Doseon-dong | Statement: [Seongdong District, containsNeighborhood, Doseon-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doseon-dong Context triple: [Seongdong District, containsNeighborhood, Doseon-dong]
-
A.
Sogyeok-dong
Sogyeok-dong is a neighborhood in central Seoul, South Korea, known for its traditional Korean houses (hanok), art galleries, and proximity to historic palaces.
-
B.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
C.
Sogong-dong
Sogong-dong is a central neighborhood in Seoul known for its major hotels, shopping areas, and proximity to key business and cultural sites.
-
D.
Deungchon-dong
Deungchon-dong is a neighborhood in Seoul, South Korea, known for its residential areas, local markets, and convenient urban amenities.
-
E.
Seongsu-dong
Seongsu-dong is a trendy neighborhood in Seoul known for its converted industrial spaces, hip cafés, and vibrant arts and culture scene.
- 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: Doseon-dong Triple: [Seongdong District, containsNeighborhood, Doseon-dong]
Generated description
Doseon-dong is a neighborhood located within Seongdong District in Seoul, South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doseon-dong Target entity description: Doseon-dong is a neighborhood located within Seongdong District in Seoul, South Korea.
-
A.
Sogyeok-dong
Sogyeok-dong is a neighborhood in central Seoul, South Korea, known for its traditional Korean houses (hanok), art galleries, and proximity to historic palaces.
-
B.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
C.
Sogong-dong
Sogong-dong is a central neighborhood in Seoul known for its major hotels, shopping areas, and proximity to key business and cultural sites.
-
D.
Deungchon-dong
Deungchon-dong is a neighborhood in Seoul, South Korea, known for its residential areas, local markets, and convenient urban amenities.
-
E.
Seongsu-dong
Seongsu-dong is a trendy neighborhood in Seoul known for its converted industrial spaces, hip cafés, and vibrant arts and culture scene.
- 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_69da6276bcf48190aabbf279192a5fb4 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6632ee4d48190b9de3a1efa064492 |
completed | April 20, 2026, 5:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a66ee34e081908c4700097e256e92 |
completed | May 18, 2026, 1:10 a.m. |
| NEDg | Description generation | batch_6a0a6ab294148190bb1066ddc7a14335 |
completed | May 18, 2026, 1:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a6b1c2d4081908430dc24a56f22b2 |
completed | May 18, 2026, 1:27 a.m. |
Created at: April 11, 2026, 3:38 p.m.