Triple
T14886561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jongno District |
E350135
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Susong-dong
Susong-dong is a neighborhood in central Seoul, South Korea, known for its mix of traditional Korean heritage sites and modern urban development.
|
E1201476
|
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: Susong-dong | Statement: [Jongno District, contains, Susong-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susong-dong Context triple: [Jongno District, contains, Susong-dong]
-
A.
Sasang-dong
Sasang-dong is a neighborhood in Busan, South Korea, known as an urban residential and commercial area within the city's Sasang District.
-
B.
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.
-
C.
Yeonsu-dong
Yeonsu-dong is a neighborhood within Incheon, South Korea, known as a residential and local commercial area of Yeonsu District.
-
D.
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.
-
E.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, 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: Susong-dong Triple: [Jongno District, contains, Susong-dong]
Generated description
Susong-dong is a neighborhood in central Seoul, South Korea, known for its mix of traditional Korean heritage sites and modern urban development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Susong-dong Target entity description: Susong-dong is a neighborhood in central Seoul, South Korea, known for its mix of traditional Korean heritage sites and modern urban development.
-
A.
Sasang-dong
Sasang-dong is a neighborhood in Busan, South Korea, known as an urban residential and commercial area within the city's Sasang District.
-
B.
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.
-
C.
Yeonsu-dong
Yeonsu-dong is a neighborhood within Incheon, South Korea, known as a residential and local commercial area of Yeonsu District.
-
D.
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.
-
E.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5f5b1c88190815f3585770cb135 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0007757ed48190813f6ab839240a15 |
completed | May 10, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_6a000bd836f081909ee477fd311c3295 |
completed | May 10, 2026, 4:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a000c33963c8190a00271fb732c5b50 |
completed | May 10, 2026, 4:40 a.m. |
Created at: April 10, 2026, 1:56 a.m.