Triple
T20417635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apna Sapna Money Money |
E500752
|
entity |
| Predicate | leadActress |
P6108
|
FINISHED |
| Object | Koena Mitra |
—
|
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: Koena Mitra | Statement: [Apna Sapna Money Money, leadActress, Koena Mitra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koena Mitra Context triple: [Apna Sapna Money Money, leadActress, Koena Mitra]
-
A.
Koena Mitra
chosen
Koena Mitra is an Indian actress and model best known for her work in Bollywood films and popular item numbers in the early 2000s.
-
B.
Tripti Mitra
Tripti Mitra was a pioneering Indian actress and director renowned for her influential work in Bengali theatre and early modern Indian stage productions.
-
C.
Moni Bose
Moni Bose was an Indian educator and academic known for her contributions to higher education and scholarship.
-
D.
Tithi Bhattacharya
Tithi Bhattacharya is a Marxist feminist scholar and activist best known for her work on social reproduction theory and for co-editing the influential book "Feminism for the 99%: A Manifesto."
-
E.
Arpita Chatterjee
Arpita Chatterjee is an Indian actress and public figure known for her work in Bengali cinema and her presence in the regional entertainment industry.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a44ecf48190ba5a3872af500dc8 |
completed | April 20, 2026, 7:11 p.m. |
Created at: April 16, 2026, 11:30 a.m.