Triple
T3467236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patel |
E73166
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
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."
|
E361782
|
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 Patel | Statement: [Patel, hasNotableBearer, Karan Patel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karan Patel Context triple: [Patel, hasNotableBearer, Karan Patel]
-
A.
Palak Patel
Palak Patel is a film producer known for working on major Hollywood fantasy and action films, including "Snow White and the Huntsman."
-
B.
Nick Mehta
Nick Mehta is a technology executive best known as the CEO of Gainsight and a prominent advocate and thought leader in the field of customer success.
-
C.
Naveen Andrews
Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
-
D.
Mehar Sethi
Mehar Sethi is an American screenwriter and producer known for his work on television series such as "BoJack Horseman" and "It’s Always Sunny in Philadelphia."
-
E.
Cece Parekh
Cece Parekh is a confident, stylish model and Jess Day’s best friend in the sitcom "New Girl," known for her sharp wit and evolving romantic storyline.
- 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 Patel Triple: [Patel, hasNotableBearer, Karan Patel]
Generated description
Karan Patel is an Indian television actor best known for his role as Raman Bhalla in the popular Hindi TV series "Yeh Hai Mohabbatein."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karan Patel Target entity description: Karan Patel is an Indian television actor best known for his role as Raman Bhalla in the popular Hindi TV series "Yeh Hai Mohabbatein."
-
A.
Palak Patel
Palak Patel is a film producer known for working on major Hollywood fantasy and action films, including "Snow White and the Huntsman."
-
B.
Nick Mehta
Nick Mehta is a technology executive best known as the CEO of Gainsight and a prominent advocate and thought leader in the field of customer success.
-
C.
Naveen Andrews
Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
-
D.
Mehar Sethi
Mehar Sethi is an American screenwriter and producer known for his work on television series such as "BoJack Horseman" and "It’s Always Sunny in Philadelphia."
-
E.
Cece Parekh
Cece Parekh is a confident, stylish model and Jess Day’s best friend in the sitcom "New Girl," known for her sharp wit and evolving romantic storyline.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb1090188190ac8aafd87dfaa6a7 |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3680763608190acdd146dc7c0b239 |
completed | March 13, 2026, 1:27 a.m. |
| NEDg | Description generation | batch_69b36c4d77448190abe198ec9d48597d |
completed | March 13, 2026, 1:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b36cca06d48190bc72ad2e9bd9bdb5 |
completed | March 13, 2026, 1:47 a.m. |
Created at: March 8, 2026, 3:17 p.m.