Triple
T22433119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English Vinglish |
E554546
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Kumar N. S.
Kumar N. S. is a film producer best known for his work on the acclaimed Hindi film "English Vinglish."
|
E1536414
|
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: Kumar N. S. | Statement: [English Vinglish, producer, Kumar N. S.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kumar N. S. Context triple: [English Vinglish, producer, Kumar N. S.]
-
A.
S. Kumar
S. Kumar is a cinematographer known for his work on the Hindi film "Gardish."
-
B.
N. K. Ekambaram
N. K. Ekambaram is an Indian cinematographer known for his work on major Hindi and South Indian films.
-
C.
N. Gopalakrishnan
N. Gopalakrishnan is a film editor known for his work in Indian cinema, including editing the movie "Gardish."
-
D.
E. S. L. Narasimhan
E. S. L. Narasimhan is an Indian civil servant and former intelligence officer who served as a long-tenured state governor, including in Andhra Pradesh and Telangana.
-
E.
S. Suresh
S. Suresh is a prominent materials scientist recognized for his influential research in the mechanical behavior of materials and leadership in academia and engineering.
- 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: Kumar N. S. Triple: [English Vinglish, producer, Kumar N. S.]
Generated description
Kumar N. S. is a film producer best known for his work on the acclaimed Hindi film "English Vinglish."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kumar N. S. Target entity description: Kumar N. S. is a film producer best known for his work on the acclaimed Hindi film "English Vinglish."
-
A.
S. Kumar
S. Kumar is a cinematographer known for his work on the Hindi film "Gardish."
-
B.
N. K. Ekambaram
N. K. Ekambaram is an Indian cinematographer known for his work on major Hindi and South Indian films.
-
C.
N. Gopalakrishnan
N. Gopalakrishnan is a film editor known for his work in Indian cinema, including editing the movie "Gardish."
-
D.
E. S. L. Narasimhan
E. S. L. Narasimhan is an Indian civil servant and former intelligence officer who served as a long-tenured state governor, including in Andhra Pradesh and Telangana.
-
E.
S. Suresh
S. Suresh is a prominent materials scientist recognized for his influential research in the mechanical behavior of materials and leadership in academia and engineering.
- 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a3320448190ae3931062599116e |
completed | April 29, 2026, 1:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0af0f90f5881908f0011690cb0f1e4 |
completed | May 18, 2026, 10:59 a.m. |
| NEDg | Description generation | batch_6a0af268db2881908706641ad1e41f79 |
completed | May 18, 2026, 11:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b08e411048190a368214c57c35d5e |
completed | May 18, 2026, 12:41 p.m. |
Created at: April 16, 2026, 8:47 p.m.