Liu Dan
E830512
Liu Dan was a Han dynasty imperial prince, best known as the son of Emperor Wu of Han.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Liu Dan canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9918525 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liu Dan Context triple: [Emperor Wu of Han, child, Liu Dan]
-
A.
Liu Dong
Liu Dong is a Chinese middle-distance runner best known for winning the women's 1500 metres world title in 1993 as part of coach Ma Junren's famed "Ma's Army."
-
B.
Jiang Wenli
Jiang Wenli is a renowned Chinese actress celebrated for her versatile performances in film and television dramas.
-
C.
Ye Li
Ye Li is a retired Chinese women's basketball player best known as the wife of former NBA star Yao Ming.
-
D.
Qu Dongyu
Qu Dongyu is a Chinese agronomist and politician who serves as the Director-General of the United Nations Food and Agriculture Organization, leading global efforts to combat hunger and promote sustainable agriculture.
-
E.
Liu Qin
Liu Qin was a prince of the Eastern Han dynasty, known primarily as a son of Emperor Guangwu of Han.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liu Dan Target entity description: Liu Dan was a Han dynasty imperial prince, best known as the son of Emperor Wu of Han.
-
A.
Liu Dong
Liu Dong is a Chinese middle-distance runner best known for winning the women's 1500 metres world title in 1993 as part of coach Ma Junren's famed "Ma's Army."
-
B.
Jiang Wenli
Jiang Wenli is a renowned Chinese actress celebrated for her versatile performances in film and television dramas.
-
C.
Ye Li
Ye Li is a retired Chinese women's basketball player best known as the wife of former NBA star Yao Ming.
-
D.
Qu Dongyu
Qu Dongyu is a Chinese agronomist and politician who serves as the Director-General of the United Nations Food and Agriculture Organization, leading global efforts to combat hunger and promote sustainable agriculture.
-
E.
Liu Qin
Liu Qin was a prince of the Eastern Han dynasty, known primarily as a son of Emperor Guangwu of Han.
- F. None of above. chosen
Statements (10)
| Predicate | Object |
|---|---|
| instanceOf |
Chinese prince
ⓘ
Han dynasty imperial prince ⓘ |
| country | Han dynasty NERFINISHED ⓘ |
| dynasty | Han dynasty NERFINISHED ⓘ |
| ethnicity | Han Chinese NERFINISHED ⓘ |
| father | Emperor Wu of Han NERFINISHED ⓘ |
| gender | male ⓘ |
| house | Liu family NERFINISHED ⓘ |
| notableFor | being a son of Emperor Wu of Han ⓘ |
| parent | Emperor Wu of Han 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 Dan Description of subject: Liu Dan was a Han dynasty imperial prince, best known as the son of Emperor Wu of Han.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.