Triple
T21944499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 7 Khoon Maaf |
E541900
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Ranjan Palit |
—
|
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: Ranjan Palit | Statement: [7 Khoon Maaf, cinematographyBy, Ranjan Palit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ranjan Palit Context triple: [7 Khoon Maaf, cinematographyBy, Ranjan Palit]
-
A.
Ranjan Palit
chosen
Ranjan Palit is an acclaimed Indian cinematographer and documentary filmmaker known for his visually distinctive work in parallel and independent cinema.
-
B.
Niranjan Pal
Niranjan Pal was an Indian playwright and screenwriter associated with early Indian cinema and the Indian independence movement.
-
C.
Pradip Bose
Pradip Bose is a computer engineer and researcher known for his contributions to microprocessor architecture and performance analysis, particularly at IBM.
-
D.
Shyam Laha
Shyam Laha was an Indian actor known for his work in Bengali cinema, particularly in classic films of the mid-20th century.
-
E.
Boloram Das
Boloram Das is an Indian actor known for his supporting roles in Hindi films and web series.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.