Triple
T18744501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bhalla |
E458373
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Karan Bhalla
Karan Bhalla is a relatively obscure individual whose public notability appears limited or not well-documented.
|
E1345718
|
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: Karan Bhalla | Statement: [Bhalla, hasNotableBearer, Karan Bhalla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karan Bhalla Context triple: [Bhalla, hasNotableBearer, Karan Bhalla]
-
A.
Karan Patel
Karan Patel is an Indian television actor best known for his role as Raman Bhalla in the popular Hindi TV series "Yeh Hai Mohabbatein."
-
B.
Karan Soni
Karan Soni is an Indian-American actor best known for his comedic roles in film and television, including his recurring portrayal of the anxious taxi driver Dopinder in the Deadpool franchise.
-
C.
Deven Bhojani
Deven Bhojani is an Indian actor and director best known for his work in Hindi television comedies and character roles in films.
-
D.
Shaan Hathiramani
Shaan Hathiramani is an entrepreneur best known as a co-founder of the mobile video-sharing app Socialcam.
-
E.
Michael Bhaskar
Michael Bhaskar is a British writer, publisher, and technology theorist known for his work on the impact of digital innovation and artificial intelligence on society and the future.
- 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: Karan Bhalla Triple: [Bhalla, hasNotableBearer, Karan Bhalla]
Generated description
Karan Bhalla is a relatively obscure individual whose public notability appears limited or not well-documented.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karan Bhalla Target entity description: Karan Bhalla is a relatively obscure individual whose public notability appears limited or not well-documented.
-
A.
Karan Patel
Karan Patel is an Indian television actor best known for his role as Raman Bhalla in the popular Hindi TV series "Yeh Hai Mohabbatein."
-
B.
Karan Soni
Karan Soni is an Indian-American actor best known for his comedic roles in film and television, including his recurring portrayal of the anxious taxi driver Dopinder in the Deadpool franchise.
-
C.
Deven Bhojani
Deven Bhojani is an Indian actor and director best known for his work in Hindi television comedies and character roles in films.
-
D.
Shaan Hathiramani
Shaan Hathiramani is an entrepreneur best known as a co-founder of the mobile video-sharing app Socialcam.
-
E.
Michael Bhaskar
Michael Bhaskar is a British writer, publisher, and technology theorist known for his work on the impact of digital innovation and artificial intelligence on society and the future.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e57691c8688190b225cbd88493d9d1 |
completed | April 20, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a056759222081908fe56bb8bfd42638 |
completed | May 14, 2026, 6:10 a.m. |
| NEDg | Description generation | batch_6a0568c3d824819097edb11f438230c6 |
completed | May 14, 2026, 6:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05693746848190bac92fda0cdc3dc7 |
completed | May 14, 2026, 6:18 a.m. |
Created at: April 10, 2026, 11:51 a.m.