Triple
T2565697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yonsei University |
E57345
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Sinchon-dong
Sinchon-dong is a vibrant neighborhood in Seoul, South Korea, known for its youthful atmosphere, shopping, nightlife, and concentration of major universities.
|
E344372
|
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: Sinchon-dong | Statement: [Yonsei University, locatedIn, Sinchon-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sinchon-dong Context triple: [Yonsei University, locatedIn, Sinchon-dong]
-
A.
Jeonpo-dong
Jeonpo-dong is a neighborhood in Busan, South Korea, known for its trendy cafes, boutiques, and vibrant urban culture.
-
B.
Jangjeon-dong
Jangjeon-dong is a neighborhood in Busan, South Korea, known as an administrative and residential center within Geumjeong District.
-
C.
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.
-
D.
Millak-dong
Millak-dong is a coastal neighborhood in Busan, South Korea, known for its proximity to Gwangalli Beach and vibrant urban atmosphere.
-
E.
Beomjeon-dong
Beomjeon-dong is a neighborhood (dong) located within Busanjin District in Busan, 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: Sinchon-dong Triple: [Yonsei University, locatedIn, Sinchon-dong]
Generated description
Sinchon-dong is a vibrant neighborhood in Seoul, South Korea, known for its youthful atmosphere, shopping, nightlife, and concentration of major universities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sinchon-dong Target entity description: Sinchon-dong is a vibrant neighborhood in Seoul, South Korea, known for its youthful atmosphere, shopping, nightlife, and concentration of major universities.
-
A.
Jeonpo-dong
Jeonpo-dong is a neighborhood in Busan, South Korea, known for its trendy cafes, boutiques, and vibrant urban culture.
-
B.
Jangjeon-dong
Jangjeon-dong is a neighborhood in Busan, South Korea, known as an administrative and residential center within Geumjeong District.
-
C.
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.
-
D.
Millak-dong
Millak-dong is a coastal neighborhood in Busan, South Korea, known for its proximity to Gwangalli Beach and vibrant urban atmosphere.
-
E.
Beomjeon-dong
Beomjeon-dong is a neighborhood (dong) located within Busanjin District in Busan, 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_69ab4a4ef9008190a0e6d4422b9418b7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd35ef22c8190966612cc75f69eca |
completed | March 7, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e803e994819085dae09224fae2a3 |
completed | March 12, 2026, 4:21 p.m. |
| NEDg | Description generation | batch_69b2e9b82a288190a6c1700e19966de6 |
completed | March 12, 2026, 4:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ea0d98ec8190beb74a32f46554bc |
completed | March 12, 2026, 4:30 p.m. |
Created at: March 6, 2026, 9:48 p.m.