Triple
T7730098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Srijit Mukherji |
E175226
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Vinci Da
Vinci Da is a Bengali psychological thriller film directed by Srijit Mukherji, centered on a make-up artist drawn into a series of morally complex crimes.
|
E685180
|
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: Vinci Da | Statement: [Srijit Mukherji, notableWork, Vinci Da]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vinci Da Context triple: [Srijit Mukherji, notableWork, Vinci Da]
-
A.
Vinci
Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
-
B.
Vinci
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
-
C.
Leonardo
Leonardo is the first name of Leonardo DiCaprio, the acclaimed American actor and environmental activist known for films such as Titanic and Inception.
-
D.
Leonardo
Leonardo is the katana-wielding, blue-masked leader of the Teenage Mutant Ninja Turtles in the popular comic, TV, and film franchise.
-
E.
Gilles d’Ettore
Gilles d’Ettore is a French politician who serves as the long-time mayor of the Mediterranean town of Agde in southern France.
- 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: Vinci Da Triple: [Srijit Mukherji, notableWork, Vinci Da]
Generated description
Vinci Da is a Bengali psychological thriller film directed by Srijit Mukherji, centered on a make-up artist drawn into a series of morally complex crimes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vinci Da Target entity description: Vinci Da is a Bengali psychological thriller film directed by Srijit Mukherji, centered on a make-up artist drawn into a series of morally complex crimes.
-
A.
Vinci
Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
-
B.
Vinci
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
-
C.
Leonardo
Leonardo is the first name of Leonardo DiCaprio, the acclaimed American actor and environmental activist known for films such as Titanic and Inception.
-
D.
Leonardo
Leonardo is the katana-wielding, blue-masked leader of the Teenage Mutant Ninja Turtles in the popular comic, TV, and film franchise.
-
E.
Gilles d’Ettore
Gilles d’Ettore is a French politician who serves as the long-time mayor of the Mediterranean town of Agde in southern France.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703358cf881909df8496d943d6de7 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b52e176481908595fea4ace7a607 |
completed | March 29, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69c8b69c1644819097b84ba84fac9cd2 |
completed | March 29, 2026, 5:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8ba9115a08190b569931686a9cd8d |
completed | March 29, 2026, 5:37 a.m. |
Created at: March 27, 2026, 4:06 p.m.