Liu Xiang
E719110
Liu Xiang was a Chinese educator and politician active in the early 20th century who played a key role in developing higher education in China.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Liu Xiang canonical | 2 |
Statements (15)
| Predicate | Object |
|---|---|
| instanceOf |
educator
ⓘ
human ⓘ politician ⓘ |
| activeInPeriod | early 20th century ⓘ |
| countryOfCitizenship | China ⓘ |
| ethnicGroup | Han Chinese ⓘ |
| fieldOfWork |
education
ⓘ
politics ⓘ |
| gender | male ⓘ |
| languageOfWorkOrName | Chinese ⓘ |
| notableActivity | promoting higher education reforms in China ⓘ |
| notableFor | development of higher education in China ⓘ |
| occupation |
educator
ⓘ
politician ⓘ |
| workLocation | China NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Liu Xiang Description of subject: Liu Xiang was a Chinese educator and politician active in the early 20th century who played a key role in developing higher education in China.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.