Triple
T16589074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smita Patil |
E403034
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Prateik Babbar
Prateik Babbar is an Indian film actor known for his roles in Hindi cinema, including his debut in "Jaane Tu... Ya Jaane Na."
|
E1236599
|
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: Prateik Babbar | Statement: [Smita Patil, child, Prateik Babbar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prateik Babbar Context triple: [Smita Patil, child, Prateik Babbar]
-
A.
Tusshar Kapoor
Tusshar Kapoor is an Indian film actor and producer known for his work in Bollywood comedies such as the "Golmaal" series.
-
B.
Ishaan Khatter
Ishaan Khatter is an Indian film actor known for his work in Hindi cinema, including acclaimed performances in films like "Beyond the Clouds" and "Dhadak."
-
C.
Aditya Sood
Aditya Sood is a film producer known for his work on high-profile Hollywood projects, including the thriller-comedy "Cocaine Bear."
-
D.
Sacha Dhawan
Sacha Dhawan is a British actor known for his versatile television and film roles, including his acclaimed portrayal of the Master in the long-running sci-fi series Doctor Who.
-
E.
Arjun Kapoor
Arjun Kapoor is an Indian film actor known for his work in Bollywood movies such as "Ishaqzaade," "2 States," and "Gunday."
- 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: Prateik Babbar Triple: [Smita Patil, child, Prateik Babbar]
Generated description
Prateik Babbar is an Indian film actor known for his roles in Hindi cinema, including his debut in "Jaane Tu... Ya Jaane Na."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prateik Babbar Target entity description: Prateik Babbar is an Indian film actor known for his roles in Hindi cinema, including his debut in "Jaane Tu... Ya Jaane Na."
-
A.
Tusshar Kapoor
Tusshar Kapoor is an Indian film actor and producer known for his work in Bollywood comedies such as the "Golmaal" series.
-
B.
Ishaan Khatter
Ishaan Khatter is an Indian film actor known for his work in Hindi cinema, including acclaimed performances in films like "Beyond the Clouds" and "Dhadak."
-
C.
Aditya Sood
Aditya Sood is a film producer known for his work on high-profile Hollywood projects, including the thriller-comedy "Cocaine Bear."
-
D.
Sacha Dhawan
Sacha Dhawan is a British actor known for his versatile television and film roles, including his acclaimed portrayal of the Master in the long-running sci-fi series Doctor Who.
-
E.
Arjun Kapoor
Arjun Kapoor is an Indian film actor known for his work in Bollywood movies such as "Ishaqzaade," "2 States," and "Gunday."
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599e79288190b6bcdb6fe4a2d1fa |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb00b14c819093925c109913322c |
completed | May 10, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_6a00bbc80d54819092de4ee363508b49 |
completed | May 10, 2026, 5:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00bc633abc8190a86808986ba294ec |
completed | May 10, 2026, 5:12 p.m. |
Created at: April 10, 2026, 5:16 a.m.