Triple
T20417400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jhankaar Beats |
E500748
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object |
Sujoy Ghosh
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.
|
E1481259
|
NE FINISHED |
How this triple was built (4 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, director, Sujoy Ghosh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sujoy Ghosh Context triple: [Jhankaar Beats, director, Sujoy Ghosh]
-
A.
Joydeep Ghosh
Joydeep Ghosh is a computer science professor and researcher known for his work in machine learning, data mining, and pattern recognition.
-
B.
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.
-
C.
Partha Ghosh
Partha Ghosh is a notable individual recognized for prominently bearing the surname Ghosh.
-
D.
Sabyasachi Saha
Sabyasachi Saha is an individual notable enough to be specifically distinguished as a bearer of the surname Saha.
-
E.
Subrata Saha
Subrata Saha is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the surname Saha.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sujoy Ghosh Triple: [Jhankaar Beats, director, Sujoy Ghosh]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sujoy Ghosh Target entity description: 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.
-
A.
Joydeep Ghosh
Joydeep Ghosh is a computer science professor and researcher known for his work in machine learning, data mining, and pattern recognition.
-
B.
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.
-
C.
Partha Ghosh
Partha Ghosh is a notable individual recognized for prominently bearing the surname Ghosh.
-
D.
Sabyasachi Saha
Sabyasachi Saha is an individual notable enough to be specifically distinguished as a bearer of the surname Saha.
-
E.
Subrata Saha
Subrata Saha is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the surname Saha.
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09b409caac819085163e0d2dc3a020 |
completed | May 17, 2026, 12:26 p.m. |
| NEDg | Description generation | batch_6a09b5e6d1ac8190aeec88859d17d257 |
completed | May 17, 2026, 12:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09b699fef081909797a5fc50814cb9 |
completed | May 17, 2026, 12:37 p.m. |
Created at: April 16, 2026, 11:30 a.m.