Triple
T16535388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suwon |
E401676
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Suwon Hyanggyo
Suwon Hyanggyo is a historic Confucian state school in Suwon, South Korea, known for educating scholars and conducting ancestral rites during the Joseon Dynasty.
|
E1217838
|
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: Suwon Hyanggyo | Statement: [Suwon, hasLandmark, Suwon Hyanggyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suwon Hyanggyo Context triple: [Suwon, hasLandmark, Suwon Hyanggyo]
-
A.
Won-dong
Won-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
B.
Sanggyeong
Sanggyeong was the principal royal capital city of the Balhae kingdom, serving as its political and cultural center in Northeast Asia.
-
C.
Park In-chon
Park In-chon was a South Korean entrepreneur best known as the founder of the Kumho Asiana business conglomerate.
-
D.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
-
E.
Gaegyeong
Gaegyeong was the principal royal and administrative capital of the Korean kingdom of Goryeo, located in what is now Kaesong, North 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: Suwon Hyanggyo Triple: [Suwon, hasLandmark, Suwon Hyanggyo]
Generated description
Suwon Hyanggyo is a historic Confucian state school in Suwon, South Korea, known for educating scholars and conducting ancestral rites during the Joseon Dynasty.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suwon Hyanggyo Target entity description: Suwon Hyanggyo is a historic Confucian state school in Suwon, South Korea, known for educating scholars and conducting ancestral rites during the Joseon Dynasty.
-
A.
Won-dong
Won-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
B.
Sanggyeong
Sanggyeong was the principal royal capital city of the Balhae kingdom, serving as its political and cultural center in Northeast Asia.
-
C.
Park In-chon
Park In-chon was a South Korean entrepreneur best known as the founder of the Kumho Asiana business conglomerate.
-
D.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
-
E.
Gaegyeong
Gaegyeong was the principal royal and administrative capital of the Korean kingdom of Goryeo, located in what is now Kaesong, North 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e345574d88819094548367bf983078 |
completed | April 18, 2026, 8:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006094dee481908757b84c10d0dc19 |
completed | May 10, 2026, 10:40 a.m. |
| NEDg | Description generation | batch_6a0060eca9fc81908f376cd2f7219bcd |
completed | May 10, 2026, 10:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00622f44ec8190a7d66c7882ae28b2 |
completed | May 10, 2026, 10:47 a.m. |
Created at: April 10, 2026, 5:15 a.m.