Triple
T29162055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don (1978 film) |
E739212
|
entity |
| Predicate | leadFemaleRole |
P6108
|
FINISHED |
| Object | Zeenat Aman as Roma |
—
|
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: Zeenat Aman as Roma | Statement: [Don (1978 film), leadFemaleRole, Zeenat Aman as Roma]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadFemaleRole Context triple: [Don (1978 film), leadFemaleRole, Zeenat Aman as Roma]
-
A.
leadActress
chosen
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
femaleRole
Indicates that the role, function, or position involved is associated with or designated as female.
-
C.
leadCharacterCaste
Indicates that the lead character in a work belongs to a specified caste.
-
D.
femaleLeadCharacterStatus
Indicates the narrative or role status assigned to a female lead character within a story or production.
-
E.
leadActorRolePattern
Indicates a recurring or characteristic type of role that an actor typically plays as a leading performer in productions.
- 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_69f07cb528fc8190a556b73990c347c8 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f662d2056081909eb1b5fdd188816b |
completed | May 2, 2026, 8:47 p.m. |
| PD | Predicate disambiguation | batch_69f65c24f8b48190af81b575f3c15be5 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 11:48 a.m.