Triple
T21944261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talvar |
E541894
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Konkona Sen Sharma |
—
|
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: Konkona Sen Sharma | Statement: [Talvar, leadActor, Konkona Sen Sharma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konkona Sen Sharma Context triple: [Talvar, leadActor, Konkona Sen Sharma]
-
A.
Konkona Sen Sharma
chosen
Konkona Sen Sharma is an acclaimed Indian actress and filmmaker known for her powerful performances in parallel and mainstream Hindi and Bengali cinema.
-
B.
Vidya Balan
Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
-
C.
Kangana Ranaut
Kangana Ranaut is an acclaimed Indian film actress known for her powerful performances in Hindi cinema and multiple National Film Awards.
-
D.
Rituparna Sengupta
Rituparna Sengupta is a prominent Indian film actress best known for her extensive and acclaimed work in Bengali 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.