Triple
T7660245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tollywood |
E173485
|
entity |
| Predicate | hasNotableDirector |
P4744
|
FINISHED |
| Object | Aparna Sen |
E173486
|
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: Aparna Sen | Statement: [Tollywood, hasNotableDirector, Aparna Sen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aparna Sen Context triple: [Tollywood, hasNotableDirector, Aparna Sen]
-
A.
Aparna Sen
chosen
Aparna Sen is an acclaimed Indian filmmaker, screenwriter, and actress known for her pioneering and nuanced work in Bengali cinema.
-
B.
Suchitra Sen
Suchitra Sen was a legendary Indian film actress renowned for her powerful performances in Bengali cinema and as the first Indian actress to receive an international film award.
-
C.
Sharmila Tagore
Sharmila Tagore is an acclaimed Indian actress known for her influential work in both Bengali art cinema and mainstream Hindi films since the 1960s.
-
D.
Gita Sen
Gita Sen is an Indian actress known for her frequent collaborations with her husband, acclaimed filmmaker Mrinal Sen, in Bengali parallel cinema.
-
E.
Smita Patil
Smita Patil was a critically acclaimed Indian actress known for her powerful performances in parallel cinema during the 1970s and 1980s.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a47a5c8190867e39f552c86787 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b14b6848190892a262903d78b79 |
completed | March 29, 2026, 3:23 a.m. |
Created at: March 27, 2026, 3:59 p.m.