Triple
T19576102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sardari Begum |
E489862
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Rajit Kapur |
—
|
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: Rajit Kapur | Statement: [Sardari Begum, castMember, Rajit Kapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rajit Kapur Context triple: [Sardari Begum, castMember, Rajit Kapur]
-
A.
Rajit Kapur
chosen
Rajit Kapur is an Indian actor acclaimed for his nuanced performances in film, television, and theatre, notably in both parallel and mainstream cinema.
-
B.
Pankaj Kapur
Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
-
C.
Randhir Kapoor
Randhir Kapoor is an Indian actor, director, and producer from the prominent Kapoor film family, known for his work in Hindi cinema since the 1970s.
-
D.
Randeep Hooda
Randeep Hooda is an Indian film actor known for his intense performances in Hindi cinema across critically acclaimed and commercially successful films.
-
E.
Vikas Khanna
Vikas Khanna is an acclaimed Indian chef, restaurateur, cookbook author, and filmmaker known for his Michelin-starred cooking and appearances on culinary television shows.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e64024c5b08190bbff6df633857874 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.