Triple
T10548285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A. S. Dileep Kumar |
E248877
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Dileep
Dileep is an Indian film actor and producer best known for his work in Malayalam cinema.
|
E887109
|
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: Dileep | Statement: [A. S. Dileep Kumar, givenName, Dileep]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dileep Context triple: [A. S. Dileep Kumar, givenName, Dileep]
-
A.
Madhavan
Madhavan is an Indian film actor best known for his work in Hindi and Tamil cinema, including a notable role in the acclaimed film "Rang De Basanti."
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Prakash Raj
Prakash Raj is an acclaimed Indian actor, filmmaker, and producer known for his versatile performances across multiple South Indian film industries and Hindi cinema.
-
D.
Vijay Sethupathi
Vijay Sethupathi is a critically acclaimed Indian actor, primarily known for his versatile performances in Tamil cinema across a wide range of complex and unconventional roles.
-
E.
Vijay
Vijay is a leading Indian film actor and playback singer, predominantly known for his work in Tamil cinema and his massive fan following across South India.
- 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: Dileep Triple: [A. S. Dileep Kumar, givenName, Dileep]
Generated description
Dileep is an Indian film actor and producer best known for his work in Malayalam cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dileep Target entity description: Dileep is an Indian film actor and producer best known for his work in Malayalam cinema.
-
A.
Madhavan
Madhavan is an Indian film actor best known for his work in Hindi and Tamil cinema, including a notable role in the acclaimed film "Rang De Basanti."
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Prakash Raj
Prakash Raj is an acclaimed Indian actor, filmmaker, and producer known for his versatile performances across multiple South Indian film industries and Hindi cinema.
-
D.
Vijay Sethupathi
Vijay Sethupathi is a critically acclaimed Indian actor, primarily known for his versatile performances in Tamil cinema across a wide range of complex and unconventional roles.
-
E.
Vijay
Vijay is a leading Indian film actor and playback singer, predominantly known for his work in Tamil cinema and his massive fan following across South India.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d526d305d081909b48d244e1cfa092 |
completed | April 7, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de843dfe708190a14dc54be56b112d |
completed | April 14, 2026, 6:15 p.m. |
| NEDg | Description generation | batch_69de8954500c81909b57c4f8007959aa |
completed | April 14, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de8f38e3048190b1acc81bb56fe165 |
completed | April 14, 2026, 7:02 p.m. |
Created at: April 6, 2026, 12:33 p.m.