Triple
T21944127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hindi Medium |
E541892
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object |
Saket Chaudhary
Saket Chaudhary is an Indian film director and screenwriter known for his work in contemporary Hindi cinema, including socially themed comedies and dramas.
|
E1520555
|
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: Saket Chaudhary | Statement: [Hindi Medium, director, Saket Chaudhary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saket Chaudhary Context triple: [Hindi Medium, director, Saket Chaudhary]
-
A.
Sanjay Chouhan
Sanjay Chouhan is an Indian screenwriter best known for co-writing the acclaimed biographical film "Paan Singh Tomar."
-
B.
Raghuveer Chaudhari
Raghuveer Chaudhari is an acclaimed Indian Gujarati writer and scholar renowned for his influential novels, poetry, and literary criticism.
-
C.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
D.
Mukesh Chhabra
Mukesh Chhabra is an Indian casting director and filmmaker known for his work on numerous prominent Bollywood films.
-
E.
Vijay Kumar Choudhary
Vijay Kumar Choudhary is an Indian politician from Bihar who has held key ministerial portfolios in the state government, including finance.
- 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: Saket Chaudhary Triple: [Hindi Medium, director, Saket Chaudhary]
Generated description
Saket Chaudhary is an Indian film director and screenwriter known for his work in contemporary Hindi cinema, including socially themed comedies and dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saket Chaudhary Target entity description: Saket Chaudhary is an Indian film director and screenwriter known for his work in contemporary Hindi cinema, including socially themed comedies and dramas.
-
A.
Sanjay Chouhan
Sanjay Chouhan is an Indian screenwriter best known for co-writing the acclaimed biographical film "Paan Singh Tomar."
-
B.
Raghuveer Chaudhari
Raghuveer Chaudhari is an acclaimed Indian Gujarati writer and scholar renowned for his influential novels, poetry, and literary criticism.
-
C.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
D.
Mukesh Chhabra
Mukesh Chhabra is an Indian casting director and filmmaker known for his work on numerous prominent Bollywood films.
-
E.
Vijay Kumar Choudhary
Vijay Kumar Choudhary is an Indian politician from Bihar who has held key ministerial portfolios in the state government, including finance.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242515ec8190b015bf8c7b13be85 |
completed | April 28, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a8f0650448190897c27ae4de1dbdf |
completed | May 18, 2026, 4:01 a.m. |
| NEDg | Description generation | batch_6a0a935f48748190a217e72780635460 |
completed | May 18, 2026, 4:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a93e870888190b7ce0bfd1b74bcef |
completed | May 18, 2026, 4:22 a.m. |
Created at: April 16, 2026, 7:56 p.m.