Triple
T22102929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ram Lakhan |
E546213
|
entity |
| Predicate | screenplayBy |
P15305
|
FINISHED |
| Object |
Ram Kelkar
Ram Kelkar is an Indian screenwriter best known for his work on popular Hindi films such as the 1989 action-comedy "Ram Lakhan."
|
E1552101
|
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: Ram Kelkar | Statement: [Ram Lakhan, screenplayBy, Ram Kelkar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ram Kelkar Context triple: [Ram Lakhan, screenplayBy, Ram Kelkar]
-
A.
Amol Palekar
Amol Palekar is an acclaimed Indian actor and director known for his understated, middle-class everyman roles in Hindi and Marathi cinema, particularly during the 1970s and 1980s.
-
B.
Nana Patekar
Nana Patekar is a renowned Indian actor and filmmaker known for his intense, realistic performances in Marathi and Hindi cinema.
-
C.
Kamal Shirwadkar
Kamal Shirwadkar was the wife of renowned Marathi poet and writer Vishnu Vaman Shirwadkar, popularly known as Kusumagraj.
-
D.
Satya Manjrekar
Satya Manjrekar is an Indian actor known for his work in Marathi and Hindi cinema, and for being the son of filmmaker-actor Mahesh Manjrekar.
-
E.
Anant Nag
Anant Nag is a renowned Indian actor known primarily for his work in Kannada cinema, acclaimed for his versatile performances in both parallel and mainstream films.
- 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: Ram Kelkar Triple: [Ram Lakhan, screenplayBy, Ram Kelkar]
Generated description
Ram Kelkar is an Indian screenwriter best known for his work on popular Hindi films such as the 1989 action-comedy "Ram Lakhan."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ram Kelkar Target entity description: Ram Kelkar is an Indian screenwriter best known for his work on popular Hindi films such as the 1989 action-comedy "Ram Lakhan."
-
A.
Amol Palekar
Amol Palekar is an acclaimed Indian actor and director known for his understated, middle-class everyman roles in Hindi and Marathi cinema, particularly during the 1970s and 1980s.
-
B.
Nana Patekar
Nana Patekar is a renowned Indian actor and filmmaker known for his intense, realistic performances in Marathi and Hindi cinema.
-
C.
Kamal Shirwadkar
Kamal Shirwadkar was the wife of renowned Marathi poet and writer Vishnu Vaman Shirwadkar, popularly known as Kusumagraj.
-
D.
Satya Manjrekar
Satya Manjrekar is an Indian actor known for his work in Marathi and Hindi cinema, and for being the son of filmmaker-actor Mahesh Manjrekar.
-
E.
Anant Nag
Anant Nag is a renowned Indian actor known primarily for his work in Kannada cinema, acclaimed for his versatile performances in both parallel and mainstream films.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b8fabf9fc81909cb79b5caa7d5312 |
completed | May 18, 2026, 10:16 p.m. |
| NEDg | Description generation | batch_6a0b90cfe3108190bbf69c9a6041399e |
completed | May 18, 2026, 10:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b9163f1b481908cc4091b389bced4 |
completed | May 18, 2026, 10:23 p.m. |
Created at: April 16, 2026, 8:30 p.m.