Triple
T26150309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vakeel Saab |
E659794
|
entity |
| Predicate | leadFemaleActorForCharacter |
P6108
|
FINISHED |
| Object | Nivetha Thomas as Pallavi |
—
|
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: Nivetha Thomas as Pallavi | Statement: [Vakeel Saab, leadFemaleActorForCharacter, Nivetha Thomas as Pallavi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadFemaleActorForCharacter Context triple: [Vakeel Saab, leadFemaleActorForCharacter, Nivetha Thomas as Pallavi]
-
A.
leadActress
chosen
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
femaleLeadCharacterStatus
Indicates the narrative or role status assigned to a female lead character within a story or production.
-
C.
relationshipTypeWithFemaleLead
Indicates the type or nature of a relationship that an entity has with a female lead.
-
D.
voiceActorFemale
Indicates that the subject is a female voice actor who provides the voice for the specified character or role.
-
E.
hasFemaleTitleCharacter
Indicates that the subject work includes at least one female character whose title or role is explicitly referenced in its title.
- 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_69ee5bc496a88190af7deb7ab5e081de |
completed | April 26, 2026, 6:39 p.m. |
| NER | Named-entity recognition | batch_69f60c0a164c819098ef0266d84c3bdf |
completed | May 2, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69f5b0021da88190bdd4cf2698c23edf |
completed | May 2, 2026, 8:04 a.m. |
Created at: April 26, 2026, 8:24 p.m.