Triple
T14841429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SKEMA Business School |
E348973
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Suzhou |
E107819
|
NE FINISHED |
How this triple was built (2 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: Suzhou | Statement: [SKEMA Business School, hasCampusIn, Suzhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzhou Context triple: [SKEMA Business School, hasCampusIn, Suzhou]
-
A.
Suzhou
chosen
Suzhou is a historic and economically significant city in eastern China, renowned for its classical gardens, canals, and silk industry.
-
B.
Wuxi
Wuxi is a major industrial and cultural city in eastern China, located near Lake Tai and known for its manufacturing, canals, and historic gardens.
-
C.
Zhenjiang
Zhenjiang is a historic port city in eastern China known for its strategic location on the Yangtze River and its rich cultural and culinary heritage.
-
D.
Yangzhou
Yangzhou is a historic city in eastern China renowned for its canals, gardens, and role as a major cultural and commercial center along the Grand Canal.
-
E.
Changshu
Changshu is a county-level city in Jiangsu Province, eastern China, known for its textile industry, historic sites, and location near Suzhou and Shanghai.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded28fa49c81908d1059e6cafd607f |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bc9db7c8190af08b26471d28e97 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 1:53 a.m.