Triple
T7694387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deepika Padukone |
E174333
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object |
Prakash Padukone
Prakash Padukone is a former Indian badminton champion widely regarded as one of the country’s greatest players and a pioneer of the sport in India.
|
E683495
|
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: Prakash Padukone | Statement: [Deepika Padukone, parent, Prakash Padukone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prakash Padukone Context triple: [Deepika Padukone, parent, Prakash Padukone]
-
A.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
-
B.
Vikas Khanna
Vikas Khanna is an acclaimed Indian chef, restaurateur, cookbook author, and filmmaker known for his Michelin-starred cooking and appearances on culinary television shows.
-
C.
Pankaj Kapur
Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
-
D.
Paresh Rawal
Paresh Rawal is a renowned Indian actor and comedian celebrated for his versatile performances in Hindi cinema and theatre.
-
E.
Sanjay Kapoor
Sanjay Kapoor is an Indian film and television actor and producer known for his work in Hindi cinema since the 1990s.
- 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: Prakash Padukone Triple: [Deepika Padukone, parent, Prakash Padukone]
Generated description
Prakash Padukone is a former Indian badminton champion widely regarded as one of the country’s greatest players and a pioneer of the sport in India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prakash Padukone Target entity description: Prakash Padukone is a former Indian badminton champion widely regarded as one of the country’s greatest players and a pioneer of the sport in India.
-
A.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
-
B.
Vikas Khanna
Vikas Khanna is an acclaimed Indian chef, restaurateur, cookbook author, and filmmaker known for his Michelin-starred cooking and appearances on culinary television shows.
-
C.
Pankaj Kapur
Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
-
D.
Paresh Rawal
Paresh Rawal is a renowned Indian actor and comedian celebrated for his versatile performances in Hindi cinema and theatre.
-
E.
Sanjay Kapoor
Sanjay Kapoor is an Indian film and television actor and producer known for his work in Hindi cinema since the 1990s.
- 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702459f988190bf7087bf51d5317f |
completed | March 27, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acaa6004819088f1ae45ad9b378e |
completed | March 29, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69c8adf82b5481908bb556a15ff942fd |
completed | March 29, 2026, 4:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8ae9096ac8190af6fdfbfc35200cd |
completed | March 29, 2026, 4:46 a.m. |
Created at: March 27, 2026, 4:02 p.m.