Girish Gangadharan
E746033
Girish Gangadharan is an Indian cinematographer known for his visually dynamic work in contemporary Malayalam and Tamil cinema.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Girish Gangadharan canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T8559080 — 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: Girish Gangadharan Context triple: [Vikram (2022 film), cinematographyBy, Girish Gangadharan]
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A.
Girish Sastry
Girish Sastry is an AI researcher and entrepreneur known for his work at OpenAI, including contributions to multimodal models such as CLIP.
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B.
Sanjay Kallapur
Sanjay Kallapur is an Indian economist and academic known for his contributions to accounting and finance research and his association with leading institutions such as the Delhi School of Economics.
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C.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
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D.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
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E.
Vijay Karunamurthy
Vijay Karunamurthy is a technology entrepreneur best known for co-founding the live video food platform Nom.com and for his leadership roles at major tech companies.
- 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: Girish Gangadharan Target entity description: Girish Gangadharan is an Indian cinematographer known for his visually dynamic work in contemporary Malayalam and Tamil cinema.
-
A.
Girish Sastry
Girish Sastry is an AI researcher and entrepreneur known for his work at OpenAI, including contributions to multimodal models such as CLIP.
-
B.
Sanjay Kallapur
Sanjay Kallapur is an Indian economist and academic known for his contributions to accounting and finance research and his association with leading institutions such as the Delhi School of Economics.
-
C.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
-
D.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
-
E.
Vijay Karunamurthy
Vijay Karunamurthy is a technology entrepreneur best known for co-founding the live video food platform Nom.com and for his leadership roles at major tech companies.
- F. None of above. chosen
Statements (44)
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: Girish Gangadharan Description of subject: Girish Gangadharan is an Indian cinematographer known for his visually dynamic work in contemporary Malayalam and Tamil cinema.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.