Triple
T10936697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Blonde |
E258350
|
entity |
| Predicate | canContrastWith |
P11289
|
FINISHED |
| Object | innocent girl-next-door archetype |
—
|
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: innocent girl-next-door archetype | Statement: [The Blonde, canContrastWith, innocent girl-next-door archetype]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canContrastWith Context triple: [The Blonde, canContrastWith, innocent girl-next-door archetype]
-
A.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
B.
createsContrastIn
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
C.
registerContrast
Indicates that an entity records or establishes a distinction or difference between two or more items or states.
-
D.
contrastCapability
Indicates a relationship where one entity’s capabilities are compared or set in opposition to another’s, highlighting differences in what they can do or achieve.
-
E.
oftenContrastedWith
chosen
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770afaa1c8190b8fced9c694c0938 |
completed | April 9, 2026, 9:26 a.m. |
| PD | Predicate disambiguation | batch_69d72e816a98819096d6c10dfb88a66a |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.