Triple
T1334790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pusan National University |
E28722
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object |
Yangsan
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
|
E159717
|
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: Yangsan | Statement: [Pusan National University, hasCampus, Yangsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangsan Context triple: [Pusan National University, hasCampus, Yangsan]
-
A.
Hanyang
Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
-
B.
Luyang
Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
-
C.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
-
D.
Xiantao
Xiantao is a county-level city in central China’s Hubei province, known for its location on the Jianghan Plain and its role as a regional agricultural and industrial center.
-
E.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
- 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: Yangsan Triple: [Pusan National University, hasCampus, Yangsan]
Generated description
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yangsan Target entity description: Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
-
A.
Hanyang
Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
-
B.
Luyang
Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
-
C.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
-
D.
Xiantao
Xiantao is a county-level city in central China’s Hubei province, known for its location on the Jianghan Plain and its role as a regional agricultural and industrial center.
-
E.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1eb119881909dd5fbf728d9e8ba |
completed | March 1, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acde12d0dc81908a09c0221b8db3f6 |
completed | March 8, 2026, 2:25 a.m. |
| NEDg | Description generation | batch_69acde7990b4819082a1bcb50215d4f1 |
completed | March 8, 2026, 2:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acdee2d71481908a735d5685693ca8 |
completed | March 8, 2026, 2:28 a.m. |
Created at: March 1, 2026, 7:55 p.m.