Triple
T21428699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krantiveer |
E528626
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Nana Patekar |
—
|
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: Nana Patekar | Statement: [Krantiveer, starring, Nana Patekar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nana Patekar Context triple: [Krantiveer, starring, 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.
Madan Babu
Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
-
C.
Gulshan Grover
Gulshan Grover is an Indian film actor, popularly known as Bollywood’s “Bad Man” for his numerous villainous roles across Hindi cinema.
-
D.
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.
-
E.
Amol Palekar
Amol Palekar is an acclaimed Indian actor and director known for his understated, middle-class everyman roles in Hindi and Marathi cinema, particularly during the 1970s and 1980s.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:49 p.m.