Triple
T8451055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blonde (2022 film) |
E199797
|
entity |
| Predicate | hasFictionalizationLevel |
P64759
|
FINISHED |
| Object | highly fictionalized |
—
|
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: highly fictionalized | Statement: [Blonde (2022 film), hasFictionalizationLevel, highly fictionalized]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalizationLevel Context triple: [Blonde (2022 film), hasFictionalizationLevel, highly fictionalized]
-
A.
fictionalizationLevel
chosen
Indicates the degree to which an event, account, or representation has been altered, embellished, or invented relative to factual reality.
-
B.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
C.
fictionalizationOf
Indicates that one entity is a fictional or dramatized representation, adaptation, or reimagining of another (typically real or earlier) entity or event.
-
D.
hasFictionalFunction
Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
-
E.
fictionalSecurityLevel
Indicates the degree or category of security status assigned within a fictional or imagined context.
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe44815488190a912d63512e19af0 |
completed | March 31, 2026, 3:12 p.m. |
| PD | Predicate disambiguation | batch_69cbd0fc634481909842c0a30077bfde |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:09 p.m.