Triple
T20417401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jhankaar Beats |
E500748
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Sujoy Ghosh |
—
|
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: Sujoy Ghosh | Statement: [Jhankaar Beats, writer, Sujoy Ghosh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sujoy Ghosh Context triple: [Jhankaar Beats, writer, Sujoy Ghosh]
-
A.
Sujoy Ghosh
chosen
Sujoy Ghosh is an Indian film director and screenwriter best known for acclaimed thrillers like "Kahaani" and its sequel, as well as his work in Hindi cinema and streaming content.
-
B.
Joydeep Ghosh
Joydeep Ghosh is a computer science professor and researcher known for his work in machine learning, data mining, and pattern recognition.
-
C.
Sudip Bandyopadhyay
Sudip Bandyopadhyay is an Indian politician and long-time parliamentarian from West Bengal known for his senior leadership role in the Trinamool Congress.
-
D.
Partha Ghosh
Partha Ghosh is a notable individual recognized for prominently bearing the surname Ghosh.
-
E.
Sabyasachi Saha
Sabyasachi Saha is an individual notable enough to be specifically distinguished as a bearer of the surname Saha.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a44ecf48190ba5a3872af500dc8 |
completed | April 20, 2026, 7:11 p.m. |
Created at: April 16, 2026, 11:30 a.m.