Triple
T21584648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mapo District |
E532616
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object |
Ahyeon-dong
Ahyeon-dong is a neighborhood in Seoul, South Korea, known as a residential and commercial area within the city's central Mapo District.
|
E1585587
|
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: Ahyeon-dong | Statement: [Mapo District, hasNeighborhood, Ahyeon-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ahyeon-dong Context triple: [Mapo District, hasNeighborhood, Ahyeon-dong]
-
A.
Hoehyeon-dong
Hoehyeon-dong is a neighborhood in central Seoul, South Korea, known for its proximity to major commercial and historical areas such as Myeong-dong and Namdaemun Market.
-
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.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
D.
Yongho-dong
Yongho-dong is a neighborhood in Busan, South Korea, known as a coastal residential area within the city's southern region.
-
E.
Yeonhui-dong
Yeonhui-dong is a neighborhood in Seodaemun District, Seoul, known for its quiet residential character and proximity to major university and commercial areas like Sinchon.
- 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: Ahyeon-dong Triple: [Mapo District, hasNeighborhood, Ahyeon-dong]
Generated description
Ahyeon-dong is a neighborhood in Seoul, South Korea, known as a residential and commercial area within the city's central Mapo District.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ahyeon-dong Target entity description: Ahyeon-dong is a neighborhood in Seoul, South Korea, known as a residential and commercial area within the city's central Mapo District.
-
A.
Hoehyeon-dong
Hoehyeon-dong is a neighborhood in central Seoul, South Korea, known for its proximity to major commercial and historical areas such as Myeong-dong and Namdaemun Market.
-
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.
Seongho-dong
Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
D.
Yongho-dong
Yongho-dong is a neighborhood in Busan, South Korea, known as a coastal residential area within the city's southern region.
-
E.
Yeonhui-dong
Yeonhui-dong is a neighborhood in Seodaemun District, Seoul, known for its quiet residential character and proximity to major university and commercial areas like Sinchon.
- 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_69e0c4618bec8190bcb0feb74568cbb1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb5f2cc0819095552de70eb2ad8d |
completed | April 27, 2026, 4:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c676bbdac8190807ad999c0bd6164 |
completed | May 19, 2026, 1:36 p.m. |
| NEDg | Description generation | batch_6a0c72132a608190b44c96de39b7187a |
completed | May 19, 2026, 2:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c76bcf3f08190ae28b928b18cd007 |
completed | May 19, 2026, 2:42 p.m. |
Created at: April 16, 2026, 6:31 p.m.