Triple
T19576100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sardari Begum |
E489862
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Kirron Kher |
—
|
NE NERFINISHED |
How this triple was built (2 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: Kirron Kher | Statement: [Sardari Begum, castMember, Kirron Kher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kirron Kher Context triple: [Sardari Begum, castMember, Kirron Kher]
-
A.
Kirron Kher
chosen
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.
Rhea Kapoor
Rhea Kapoor is an Indian film producer and fashion stylist known for producing Bollywood films like "Aisha" and "Veere Di Wedding" and for her work in celebrity styling.
-
C.
Bela Malhotra
Bela Malhotra is a witty, sex-positive aspiring comedy writer and one of the central student protagonists in the TV series "The Sex Lives of College Girls."
-
D.
Sanya Malhotra
Sanya Malhotra is an Indian actress known for her acclaimed debut in the film "Dangal" and subsequent roles in Hindi cinema.
-
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e64024c5b08190bbff6df633857874 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.