Triple
T16994065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vijay TV |
E412268
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Vijay |
—
|
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: Vijay | Statement: [Vijay TV, formerName, Vijay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vijay Context triple: [Vijay TV, formerName, Vijay]
-
A.
Vijay
chosen
Vijay is a leading Indian film actor and playback singer, predominantly known for his work in Tamil cinema and his massive fan following across South India.
-
B.
Vijay
Vijay is an Indian businessman and former politician best known for his leadership of the now-defunct Kingfisher Airlines and his high-profile financial controversies.
-
C.
Prithviraj Kapoor
Prithviraj Kapoor was a pioneering Indian actor and theatre legend, regarded as one of the founding figures of Hindi cinema and the Kapoor film dynasty.
-
D.
Akshay Venkatesh
Akshay Venkatesh is an Indian-Australian mathematician renowned for his deep contributions to number theory, automorphic forms, and related areas, and is a recipient of the Fields Medal.
-
E.
Dileep
Dileep is an Indian film actor and producer best known for his work in Malayalam cinema.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d28535788190bdfcb6201a9024b5 |
completed | April 18, 2026, 6:50 p.m. |
Created at: April 10, 2026, 5:32 a.m.