Triple
T9780296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scarlett Johansson as Silken Floss |
E237349
|
entity |
| Predicate | characterArchetype |
P60013
|
FINISHED |
| Object | femme fatale |
—
|
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: femme fatale | Statement: [Scarlett Johansson as Silken Floss, characterArchetype, femme fatale]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterArchetype Context triple: [Scarlett Johansson as Silken Floss, characterArchetype, femme fatale]
-
A.
typeOfCharacter
chosen
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
B.
characterArcElement
Indicates that one element is a component or stage within a character’s overall developmental arc or transformation.
-
C.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
-
D.
characterArc
Indicates the developmental journey or transformation a character undergoes over the course of a narrative.
-
E.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
- 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_69ca84d975a08190aab25b02a89bdab3 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b0b15881909ef52d0156148c59 |
completed | April 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69cd03d77c6c81909b675955bf113320 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:27 p.m.