Triple
T21108675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mirzya (2016 film) |
E520118
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Saiyami 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: Saiyami Kher | Statement: [Mirzya (2016 film), leadActor, Saiyami Kher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saiyami Kher Context triple: [Mirzya (2016 film), leadActor, Saiyami Kher]
-
A.
Saiyami Kher
chosen
Saiyami Kher is an Indian actress and model who made her Hindi film debut in the romantic drama "Mirzya" (2016) and has since appeared in various films and web series.
-
B.
Kirron Kher
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.
-
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.
Shriya Saran
Shriya Saran is an Indian actress and model known for her work in Telugu, Tamil, and Hindi cinema, appearing in numerous commercially successful and critically acclaimed 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e720ffa998819082db225363ac3b23 |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:54 p.m.