Triple
T8268394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kunal Nayyar |
E193357
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
|
E722718
|
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: Neha Kapur | Statement: [Kunal Nayyar, spouse, Neha Kapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neha Kapur Context triple: [Kunal Nayyar, spouse, Neha Kapur]
-
A.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
B.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
C.
Seema Kapoor
Seema Kapoor is an Indian television and film actress and director, known for her work in Hindi entertainment and her marriage to the late actor Om Puri.
-
D.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
E.
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.
- 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: Neha Kapur Triple: [Kunal Nayyar, spouse, Neha Kapur]
Generated description
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neha Kapur Target entity description: Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
A.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
B.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
C.
Seema Kapoor
Seema Kapoor is an Indian television and film actress and director, known for her work in Hindi entertainment and her marriage to the late actor Om Puri.
-
D.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
E.
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.
- 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_69ca82e081d48190986beaa51f498ab9 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb794fc4208190b268bc69ff2b28a9 |
completed | March 31, 2026, 7:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd6833065c8190945e88022ad2869d |
completed | April 1, 2026, 6:47 p.m. |
| NEDg | Description generation | batch_69cd6d52763c8190891f88d62be44786 |
completed | April 1, 2026, 7:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd7df568788190a5a219baa65a6a19 |
completed | April 1, 2026, 8:20 p.m. |
Created at: March 30, 2026, 5:50 p.m.