Triple
T22599357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saans |
E574769
|
entity |
| Predicate | featuresActressInVideo |
P70373
|
FINISHED |
| Object | Katrina Kaif |
—
|
NE NERFINISHED |
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: Katrina Kaif | Statement: [Saans, featuresActressInVideo, Katrina Kaif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katrina Kaif Context triple: [Saans, featuresActressInVideo, Katrina Kaif]
-
A.
Kangana Ranaut
Kangana Ranaut is an acclaimed Indian film actress known for her powerful performances in Hindi cinema and multiple National Film Awards.
-
B.
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.
-
C.
Kriti Sanon
Kriti Sanon is an Indian actress and model known for her work in Hindi cinema, including films like "Heropanti," "Bareilly Ki Barfi," and "Mimi."
-
D.
Tamannaah Bhatia
Tamannaah Bhatia is an Indian film actress known for her prominent roles in Telugu, Tamil, and Hindi cinema.
-
E.
Radhika Madan
Radhika Madan is an Indian actress known for her work in Hindi films and television, transitioning from popular TV serials to acclaimed Bollywood roles.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Katrina Kaif Target entity description: Katrina Kaif is a prominent British-Indian Bollywood actress known for her leading roles in numerous Hindi films and her widespread popularity in Indian cinema.
-
A.
Kangana Ranaut
Kangana Ranaut is an acclaimed Indian film actress known for her powerful performances in Hindi cinema and multiple National Film Awards.
-
B.
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.
-
C.
Kriti Sanon
Kriti Sanon is an Indian actress and model known for her work in Hindi cinema, including films like "Heropanti," "Bareilly Ki Barfi," and "Mimi."
-
D.
Tamannaah Bhatia
Tamannaah Bhatia is an Indian film actress known for her prominent roles in Telugu, Tamil, and Hindi cinema.
-
E.
Radhika Madan
Radhika Madan is an Indian actress known for her work in Hindi films and television, transitioning from popular TV serials to acclaimed Bollywood roles.
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresActressInVideo Context triple: [Saans, featuresActressInVideo, Katrina Kaif]
-
A.
featuresAllFemaleCast
Indicates that the work’s cast is composed entirely of female performers, with no male cast members.
-
B.
featuresCast
chosen
Indicates that a creative work includes a particular person or group as part of its cast.
-
C.
laterFeaturedCastFrom
Indicates that one entity appears as a featured cast member in a later work, episode, or installment relative to another entity.
-
D.
filmAssociatedWith
Indicates a general relationship or connection between a film and another entity, such as a person, organization, event, or work.
-
E.
actorKnownFor
Indicates that an actor is widely recognized or notable for a particular work, role, or contribution.
- F. None of above.
Provenance (3 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626b5f5481909a104088d1c96720 |
completed | April 29, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 2:50 p.m.