Triple
T21428653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akshay Kumar |
E528625
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Rowdy Rathore |
—
|
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: Rowdy Rathore | Statement: [Akshay Kumar, notableWork, Rowdy Rathore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rowdy Rathore Context triple: [Akshay Kumar, notableWork, Rowdy Rathore]
-
A.
Rowdy Rathore
chosen
Rowdy Rathore is a 2012 Indian action masala film starring Akshay Kumar as a fearless cop who battles crime in a small town.
-
B.
Kallar
Kallar was a prominent ruler of the Hindu Shahi dynasty, known for leading this early medieval Indian kingdom in the northwestern regions of the subcontinent.
-
C.
Kallar
Kallar is a river in the Indian state of Kerala known for feeding into the Pamba River and flowing through forested, hilly terrain.
-
D.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
-
E.
Jawani Diwani
Jawani Diwani is a 1972 Hindi romantic comedy film starring Randhir Kapoor and Jaya Bhaduri, known for its youthful love story and popular music.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:49 p.m.