Triple
T5461971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vijay Dhondopant Tendulkar |
E122612
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Vijay |
E202777
|
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: Vijay | Statement: [Vijay Dhondopant Tendulkar, givenName, Vijay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vijay Context triple: [Vijay Dhondopant Tendulkar, givenName, 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.
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.
-
C.
Ajith Kumar
Ajith Kumar is a prominent Indian film actor and racing driver best known for his leading roles in Tamil cinema and his massive fan following.
-
D.
Salman Khan
Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
-
E.
Akshaye Khanna
Akshaye Khanna is an Indian film actor known for his versatile performances in Hindi cinema across both commercial hits and critically acclaimed dramas.
- 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9201dbfc8190bea22d6ecbc25b3e |
completed | March 20, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf414ebd288190ae90593232ff2db9 |
completed | March 22, 2026, 1:09 a.m. |
Created at: March 20, 2026, 2:08 p.m.