Triple
T5524574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dimple Kapadia |
E144890
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Twinkle Khanna
Twinkle Khanna is an Indian author, columnist, interior designer, and former Bollywood actress known for her witty writing and bestselling books.
|
E528624
|
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: Twinkle Khanna | Statement: [Dimple Kapadia, child, Twinkle Khanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Twinkle Khanna Context triple: [Dimple Kapadia, child, Twinkle Khanna]
-
A.
Kirron Kher
Kirron Kher is an Indian film and television actress and politician known for her powerful character roles in Hindi cinema and her work as a Member of Parliament.
-
B.
Ekta Kapoor
Ekta Kapoor is a prominent Indian television and film producer known for revolutionizing Hindi soap operas and co-founding Balaji Telefilms.
-
C.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
D.
Sonam Kapoor
Sonam Kapoor is a prominent Indian actress and fashion icon known for her work in Hindi cinema and her influential presence in the fashion industry.
-
E.
Kareen
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
- 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: Twinkle Khanna Triple: [Dimple Kapadia, child, Twinkle Khanna]
Generated description
Twinkle Khanna is an Indian author, columnist, interior designer, and former Bollywood actress known for her witty writing and bestselling books.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Twinkle Khanna Target entity description: Twinkle Khanna is an Indian author, columnist, interior designer, and former Bollywood actress known for her witty writing and bestselling books.
-
A.
Kirron Kher
Kirron Kher is an Indian film and television actress and politician known for her powerful character roles in Hindi cinema and her work as a Member of Parliament.
-
B.
Ekta Kapoor
Ekta Kapoor is a prominent Indian television and film producer known for revolutionizing Hindi soap operas and co-founding Balaji Telefilms.
-
C.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
D.
Sonam Kapoor
Sonam Kapoor is a prominent Indian actress and fashion icon known for her work in Hindi cinema and her influential presence in the fashion industry.
-
E.
Kareen
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f874bd081909cccfc25767ee6fa |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027f6aa1c8190b639c317c7d60f64 |
completed | March 22, 2026, 5:33 p.m. |
| NEDg | Description generation | batch_69c033dc91e08190888fb6e94027fbdb |
completed | March 22, 2026, 6:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03460b21481908b78aa4bdc989d2c |
completed | March 22, 2026, 6:26 p.m. |
Created at: March 22, 2026, 3:34 p.m.