Triple
T6969365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mythri Movie Makers |
E161563
|
entity |
| Predicate | hasProducer |
P30366
|
FINISHED |
| Object | Naveen Yerneni |
E632304
|
NE FINISHED |
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: Naveen Yerneni | Statement: [Mythri Movie Makers, hasProducer, Naveen Yerneni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naveen Yerneni Context triple: [Mythri Movie Makers, hasProducer, Naveen Yerneni]
-
A.
Naveen Yerneni
chosen
Naveen Yerneni is an Indian film producer and co-founder of the prominent Telugu production company Mythri Movie Makers, known for backing several major commercial and critically acclaimed films.
-
B.
Sanjay Reddy
Sanjay Reddy is an Indian economist known for his work in development economics, poverty measurement, and global justice.
-
C.
Ravi Basrur
Ravi Basrur is an Indian film music composer and sound designer best known for his work on high-profile Kannada films such as the K.G.F series.
-
D.
Rahul Banga
Rahul Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
-
E.
Rohan Murty
Rohan Murty is an Indian computer scientist, entrepreneur, and philanthropist, known for founding the Murty Classical Library of India and for his work in technology and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db152b2081909271493a5d1469fb |
completed | March 27, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a04a77c8190959056a68a349f6e |
completed | March 28, 2026, 5:41 a.m. |
Created at: March 27, 2026, 2:30 p.m.