Triple
T16791985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rajat Kapoor |
E408131
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Meghna Kapoor
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
|
E1242361
|
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: Meghna Kapoor | Statement: [Rajat Kapoor, spouse, Meghna Kapoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meghna Kapoor Context triple: [Rajat Kapoor, spouse, Meghna Kapoor]
-
A.
Sanah Kapur
Sanah Kapur is an Indian actress known for her supporting role in the film "Shaandaar" and for being part of the Kapur film family.
-
B.
Bhumika Chawla
Bhumika Chawla is an Indian actress known for her work in Hindi, Telugu, and Tamil films, including notable roles in movies like "Tere Naam" and "Gandhi, My Father."
-
C.
Kajal Aggarwal
Kajal Aggarwal is a popular Indian actress best known for her leading roles in Telugu and Tamil cinema, as well as appearances in Hindi films.
-
D.
Anshula Kapoor
Anshula Kapoor is an Indian celebrity and entrepreneur known for being part of the Kapoor film family and for founding the mental-health-focused fundraising platform Fankind.
-
E.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
- 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: Meghna Kapoor Triple: [Rajat Kapoor, spouse, Meghna Kapoor]
Generated description
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meghna Kapoor Target entity description: Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
-
A.
Sanah Kapur
Sanah Kapur is an Indian actress known for her supporting role in the film "Shaandaar" and for being part of the Kapur film family.
-
B.
Bhumika Chawla
Bhumika Chawla is an Indian actress known for her work in Hindi, Telugu, and Tamil films, including notable roles in movies like "Tere Naam" and "Gandhi, My Father."
-
C.
Kajal Aggarwal
Kajal Aggarwal is a popular Indian actress best known for her leading roles in Telugu and Tamil cinema, as well as appearances in Hindi films.
-
D.
Anshula Kapoor
Anshula Kapoor is an Indian celebrity and entrepreneur known for being part of the Kapoor film family and for founding the mental-health-focused fundraising platform Fankind.
-
E.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2a6c9888190b3f8f625b299574d |
completed | April 18, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d45013048190a8073f34820ca85a |
completed | May 10, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_6a00d51835c48190b1a37de6ac25ceaa |
completed | May 10, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d59b96108190a0e55f01529a0b64 |
completed | May 10, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:22 a.m.