Triple
T27259295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dasavathaaram |
E687711
|
entity |
| Predicate | numberOfRolesPlayedByKamalHaasan |
P32517
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Dasavathaaram, numberOfRolesPlayedByKamalHaasan, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRolesPlayedByKamalHaasan Context triple: [Dasavathaaram, numberOfRolesPlayedByKamalHaasan, 10]
-
A.
characterPlayedByAjithKumar
Indicates that a given character is portrayed or played by the actor Ajith Kumar.
-
B.
featuresActorInMultipleRoles
chosen
Indicates that a work includes an actor who portrays more than one distinct role within that same work.
-
C.
numberOfFilmsAppearedIn
Indicates the total count of distinct films in which a given entity has appeared.
-
D.
numberOfFilmfareAwards
Indicates the count of Filmfare Awards that have been received by a given entity.
-
E.
leadActorForCharacterKaran
Indicates that the specified person is the lead actor portraying the character named Karan.
- F. None of above.
Provenance (3 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_69ef35567e808190a94458cd44ebff0c |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69feafa1ba0081909013800b85a9f613 |
completed | May 9, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69feae58d62c81909d031f3df8992883 |
completed | May 9, 2026, 3:47 a.m. |
Created at: April 27, 2026, 10:51 a.m.