Triple
T25395225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madhumati |
E636271
|
entity |
| Predicate | numberOfFilmfareAwards |
P180919
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Madhumati, numberOfFilmfareAwards, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmfareAwards Context triple: [Madhumati, numberOfFilmfareAwards, 9]
-
A.
numberOfFilmfareBestActorAwards
Indicates the count of Filmfare Best Actor awards that have been received by the subject.
-
B.
nationalFilmAwardsWon
Indicates that an entity has received one or more National Film Awards, specifying a winning achievement in that award system.
-
C.
numberOfNationalFilmAwards
Indicates the total count of national film awards that have been received by the referenced entity.
-
D.
mostAwardsFilmCount
Indicates the total number of awards received by the film that holds the record for having the most awards.
-
E.
numberOfNationalFilmAwardsForBestActress
Indicates the count of times an entity has received the National Film Award for Best Actress.
- 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_69e75db263888190b77fff9e2827b9a2 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f75dc25fa08190b371faf36d9fb72c |
completed | May 3, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f758586534819083e91172f4bf5098 |
completed | May 3, 2026, 2:14 p.m. |
| PDg | Predicate description generation | batch_69f75dc140c4819085063d6c4c36ca61 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 21, 2026, 1:49 p.m.