Triple
T21945010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andhadhun |
E541911
|
entity |
| Predicate | protagonistPortrayedBy |
P9616
|
FINISHED |
| Object | Ayushmann Khurrana |
—
|
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: Ayushmann Khurrana | Statement: [Andhadhun, protagonistPortrayedBy, Ayushmann Khurrana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ayushmann Khurrana Context triple: [Andhadhun, protagonistPortrayedBy, Ayushmann Khurrana]
-
A.
Ayushmann Khurrana
chosen
Ayushmann Khurrana is an acclaimed Indian actor and singer known for his unconventional film choices and performances in Hindi cinema.
-
B.
Ravi Kishan
Ravi Kishan is an Indian actor, film producer, and politician best known for his work in Bhojpuri cinema as well as Hindi and Telugu films.
-
C.
Ranveer Singh
Ranveer Singh is a popular Indian film actor known for his energetic performances and leading roles in numerous successful Bollywood movies.
-
D.
Varun Dhawan
Varun Dhawan is a popular Indian film actor known for his work in contemporary Bollywood cinema, particularly in commercial comedies and dramas.
-
E.
Ranbir Kapoor
Ranbir Kapoor is a prominent Indian film actor and producer known for his leading roles in contemporary Hindi 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.