Triple
T16964369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tulu cinema |
E411505
|
entity |
| Predicate | notableActor |
P7010
|
FINISHED |
| Object |
Aravind Bolar
Aravind Bolar is a popular Indian actor and comedian best known for his work in Tulu-language films and theatre.
|
E1244268
|
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: Aravind Bolar | Statement: [Tulu cinema, notableActor, Aravind Bolar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aravind Bolar Context triple: [Tulu cinema, notableActor, Aravind Bolar]
-
A.
Ravi Basrur
Ravi Basrur is an Indian film music composer and sound designer best known for his work on high-profile Kannada films such as the K.G.F series.
-
B.
Karthik Bala
Karthik Bala is a video game developer and entrepreneur best known as the co-founder of the game studio Vicarious Visions.
-
C.
Ravi Jhankal
Ravi Jhankal is an Indian film, television, and theatre actor known for his character roles in critically acclaimed Hindi productions.
-
D.
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.
-
E.
Bharat Nalluri
Bharat Nalluri is a British-Indian film and television director known for stylish, character-driven works such as the romantic comedy-drama "Miss Pettigrew Lives for a Day" and the miniseries "Tsunami: The Aftermath."
- 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: Aravind Bolar Triple: [Tulu cinema, notableActor, Aravind Bolar]
Generated description
Aravind Bolar is a popular Indian actor and comedian best known for his work in Tulu-language films and theatre.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aravind Bolar Target entity description: Aravind Bolar is a popular Indian actor and comedian best known for his work in Tulu-language films and theatre.
-
A.
Ravi Basrur
Ravi Basrur is an Indian film music composer and sound designer best known for his work on high-profile Kannada films such as the K.G.F series.
-
B.
Karthik Bala
Karthik Bala is a video game developer and entrepreneur best known as the co-founder of the game studio Vicarious Visions.
-
C.
Ravi Jhankal
Ravi Jhankal is an Indian film, television, and theatre actor known for his character roles in critically acclaimed Hindi productions.
-
D.
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.
-
E.
Bharat Nalluri
Bharat Nalluri is a British-Indian film and television director known for stylish, character-driven works such as the romantic comedy-drama "Miss Pettigrew Lives for a Day" and the miniseries "Tsunami: The Aftermath."
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0a2cda88190bd574a869f0e43e9 |
completed | April 18, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dc09386c819088bdb2548f3fb5c8 |
completed | May 10, 2026, 7:27 p.m. |
| NEDg | Description generation | batch_6a0114d33cac819083d8e542ea5bc274 |
completed | May 10, 2026, 11:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0115c583608190bf07ac205399f253 |
completed | May 10, 2026, 11:33 p.m. |
Created at: April 10, 2026, 5:31 a.m.