Triple
T20351391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khandhar |
E496018
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Pankaj 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: Pankaj Kapur | Statement: [Khandhar, hasCastMember, Pankaj Kapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pankaj Kapur Context triple: [Khandhar, hasCastMember, Pankaj Kapur]
-
A.
Pankaj Kapur
chosen
Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
-
B.
Rajit Kapur
Rajit Kapur is an Indian actor acclaimed for his nuanced performances in film, television, and theatre, notably in both parallel and mainstream cinema.
-
C.
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.
-
D.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
-
E.
Pankaj Tripathi
Pankaj Tripathi is an acclaimed Indian actor known for his versatile character roles in Hindi films and web series such as Gangs of Wasseypur, Newton, and Mirzapur.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6784f8ff48190a070888786f6a989 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:24 a.m.