Triple
T21428568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rajesh Khanna |
E528623
|
entity |
| Predicate | numberOfFilmfareBestActorAwards |
P143931
|
FINISHED |
| Object | 3 |
—
|
LITERAL FINISHED |
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: 3 | Statement: [Rajesh Khanna, numberOfFilmfareBestActorAwards, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmfareBestActorAwards Context triple: [Rajesh Khanna, numberOfFilmfareBestActorAwards, 3]
-
A.
nationalFilmAwardsWon
Indicates that an entity has received one or more National Film Awards, specifying a winning achievement in that award system.
-
B.
numberOfNationalFilmAwardsForBestActress
Indicates the count of times an entity has received the National Film Award for Best Actress.
-
C.
mostAwardsFilmCount
Indicates the total number of awards received by the film that holds the record for having the most awards.
-
D.
filmfareAwardsWon
Indicates that the subject has received one or more Filmfare Awards, specifying the number or instances of such wins.
-
E.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
- F. None of above. chosen
Provenance (4 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_69e8b3e74bcc81909ad66e3c59152ffc |
completed | April 22, 2026, 11:41 a.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 5:49 p.m.