Triple
T16443168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghajini |
E399357
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Ravi K. Chandran |
E903624
|
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: Ravi K. Chandran | Statement: [Ghajini, cinematographyBy, Ravi K. Chandran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ravi K. Chandran Context triple: [Ghajini, cinematographyBy, Ravi K. Chandran]
-
A.
Ravi K. Chandran
chosen
Ravi K. Chandran is an acclaimed Indian cinematographer known for his visually striking work across Hindi and Tamil cinema.
-
B.
Rishikesha T. Krishnan
Rishikesha T. Krishnan is an Indian management scholar and academic leader known for his work on innovation and strategy, and for serving as director of leading Indian Institutes of Management.
-
C.
Raj Subramaniam
Raj Subramaniam is the President and Chief Executive Officer of FedEx Corporation, a leading global logistics and delivery services company.
-
D.
Srinivas Mohan
Srinivas Mohan is an acclaimed Indian visual effects supervisor known for his pioneering VFX work in major South Indian films.
-
E.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32cd8d2988190acb5722a15623319 |
completed | April 18, 2026, 7:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f4b738881908f8a205466397f33 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.