Triple
T22103314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Actor Prepares |
E546221
|
entity |
| Predicate | notableAlumni |
P51
|
FINISHED |
| Object | Abhishek Bachchan |
—
|
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: Abhishek Bachchan | Statement: [Actor Prepares, notableAlumni, Abhishek Bachchan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abhishek Bachchan Context triple: [Actor Prepares, notableAlumni, Abhishek Bachchan]
-
A.
Abhishek Bachchan
chosen
Abhishek Bachchan is an Indian film actor and producer known for his work in Bollywood across a range of commercial and critically acclaimed movies.
-
B.
Abhishek Pathak
Abhishek Pathak is an Indian film producer and director known for backing and helming notable Hindi films, including acclaimed thrillers and dramas.
-
C.
Tusshar Kapoor
Tusshar Kapoor is an Indian film actor and producer known for his work in Bollywood comedies such as the "Golmaal" series.
-
D.
Kunal Kapoor
Kunal Kapoor is an Indian actor known for his work in Hindi cinema, particularly for his acclaimed performance in the film "Rang De Basanti."
-
E.
Harshvardhan Kapoor
Harshvardhan Kapoor is an Indian film actor known for his work in Hindi cinema, including his debut in the critically acclaimed film "Mirzya."
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.