Triple
T8558350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nayanthara |
E202631
|
entity |
| Predicate | notableCollaboration |
P8554
|
FINISHED |
| Object | Ajith Kumar |
E205711
|
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: Ajith Kumar | Statement: [Nayanthara, notableCollaboration, Ajith Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ajith Kumar Context triple: [Nayanthara, notableCollaboration, Ajith Kumar]
-
A.
Ajith Kumar
chosen
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.
-
B.
Mahesh Babu
Mahesh Babu is a leading Indian actor and producer best known for his work in Telugu cinema, where he is celebrated for his charismatic screen presence and numerous blockbuster films.
-
C.
Vijay
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.
-
D.
Prabhas
Prabhas is an Indian film actor best known for his leading role in the blockbuster "Baahubali" series, which brought him international fame.
-
E.
Thalapathy
Thalapathy is the popular nickname of Indian Tamil film superstar Vijay, celebrated for his mass appeal and leading roles in commercial cinema.
- 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_69ca8326e6c881908ff720d6abaebdc5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9485dd88190bc2cf2adf39d48ee |
completed | March 31, 2026, 3:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb84a6988190ba6852f72c8918ca |
completed | April 2, 2026, 6:55 p.m. |
Created at: March 30, 2026, 6:20 p.m.