Triple
T20417630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apna Sapna Money Money |
E500752
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Sanjay Mishra |
—
|
NE NERFINISHED |
How this triple was built (2 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: Sanjay Mishra | Statement: [Apna Sapna Money Money, hasCastMember, Sanjay Mishra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanjay Mishra Context triple: [Apna Sapna Money Money, hasCastMember, Sanjay Mishra]
-
A.
Sanjay Mishra
chosen
Sanjay Mishra is an Indian film and television actor known for his versatile character roles and comic performances in Hindi cinema.
-
B.
Vinay Mishra
Vinay Mishra is an Indian politician serving as a Member of the Legislative Assembly (MLA) from the Dwarka constituency in Delhi.
-
C.
Ashok Mishra
Ashok Mishra is an Indian screenwriter known for his work on films such as "Welcome to Sajjanpur."
-
D.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
E.
Sanjay Verma
Sanjay Verma is a film editor known for his work on the Hindi movie "Dil Vil Pyar Vyar."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 16, 2026, 11:30 a.m.