Triple
T7730283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apur Sansar |
E175230
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Dulal Dutta |
E643171
|
NE FINISHED |
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: Dulal Dutta | Statement: [Apur Sansar, editor, Dulal Dutta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dulal Dutta Context triple: [Apur Sansar, editor, Dulal Dutta]
-
A.
Dulal Dutta
chosen
Dulal Dutta was an Indian film editor best known for his long-standing collaboration with director Satyajit Ray on several landmark Bengali films.
-
B.
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
C.
Soumitra Chatterjee
Soumitra Chatterjee was a legendary Indian actor best known for his long and acclaimed collaboration with filmmaker Satyajit Ray, particularly in the Apu film series and numerous other Bengali classics.
-
D.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
E.
Amar Nath Chatterjee
Amar Nath Chatterjee is an Indian politician who has served as the mayor of Asansol in West Bengal.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703358cf881909df8496d943d6de7 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8ef0465a4819095c34584e8f80739 |
completed | March 29, 2026, 9:21 a.m. |
Created at: March 27, 2026, 4:06 p.m.