Triple
T3269045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carine |
E68597
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Kareen
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
|
E343025
|
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: Kareen | Statement: [Carine, hasVariant, Kareen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kareen Context triple: [Carine, hasVariant, Kareen]
-
A.
Kareena Kapoor Khan
Kareena Kapoor Khan is a prominent Indian film actress known for her versatile roles in Bollywood and her influential presence in contemporary Hindi cinema.
-
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.
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.
-
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.
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.
- 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: Kareen Triple: [Carine, hasVariant, Kareen]
Generated description
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kareen Target entity description: Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
-
A.
Kareena Kapoor Khan
Kareena Kapoor Khan is a prominent Indian film actress known for her versatile roles in Bollywood and her influential presence in contemporary Hindi cinema.
-
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.
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.
-
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.
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.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafd0eddc8190834a64f6b8e8e9f9 |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28efded588190bd6c361e5298b496 |
completed | March 12, 2026, 10:01 a.m. |
| NEDg | Description generation | batch_69b28fcef05c8190b9141dcaa3f9145b |
completed | March 12, 2026, 10:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2d6c1c65c81909e661c6beaaec9af |
completed | March 12, 2026, 3:07 p.m. |
Created at: March 8, 2026, 3:09 p.m.