Triple
T5857309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marathi theatre |
E130184
|
entity |
| Predicate | hasNotableActor |
P17435
|
FINISHED |
| Object | Nana Patekar |
E563345
|
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: Nana Patekar | Statement: [Marathi theatre, hasNotableActor, Nana Patekar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nana Patekar Context triple: [Marathi theatre, hasNotableActor, Nana Patekar]
-
A.
Nana Patekar
chosen
Nana Patekar is a renowned Indian actor and filmmaker known for his intense, realistic performances in Marathi and Hindi cinema.
-
B.
Naseeruddin Shah
Naseeruddin Shah is a renowned Indian actor and director celebrated for his powerful performances in parallel cinema as well as mainstream Bollywood films.
-
C.
Anupam Kher
Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
-
D.
Anil Kapoor
Anil Kapoor is a veteran Indian actor and producer known for his work in Hindi cinema and international films, recognized for his energetic screen presence and roles in movies like "Mr. India," "Dil Dhadakne Do," and the series "24."
-
E.
Anant Nag
Anant Nag is a renowned Indian actor known primarily for his work in Kannada cinema, acclaimed for his versatile performances in both parallel and mainstream films.
- 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_69c0084f3bb08190a7720f55f7aa4252 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0355755508190ab349cdcf0c8a58d |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11cb58aec81909664a7b732519800 |
completed | March 23, 2026, 10:57 a.m. |
Created at: March 22, 2026, 3:56 p.m.