Triple
T20427567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Girl in the Café |
E501044
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Meneka Das |
—
|
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: Meneka Das | Statement: [The Girl in the Café, hasCastMember, Meneka Das]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meneka Das Context triple: [The Girl in the Café, hasCastMember, Meneka Das]
-
A.
Meneka Das
chosen
Meneka Das is an actress known for her role in the television film "The Girl in the Café" and for her work in British film and theatre.
-
B.
Tota Roy Chowdhury
Tota Roy Chowdhury is an Indian actor known for his work in Bengali cinema and Hindi films, often praised for his versatile performances and strong screen presence.
-
C.
Koena Mitra
Koena Mitra is an Indian actress and model best known for her work in Bollywood films and popular item numbers in the early 2000s.
-
D.
Smaran Ghosal
Smaran Ghosal was an Indian actor best known for playing the adult Apu in Satyajit Ray’s acclaimed film "Aparajito," part of the Apu Trilogy.
-
E.
Sabitri Chatterjee
Sabitri Chatterjee is a renowned Indian actress celebrated for her prolific work in Bengali cinema and theatre, particularly during the golden era of Tollywood.
- 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_69e0b4aa68fc8190b1a14c55575ef04a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67ba9700481909fa23493f98095d1 |
completed | April 20, 2026, 7:16 p.m. |
Created at: April 16, 2026, 11:31 a.m.