Triple
T25917299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Arts High School |
E653067
|
entity |
| Predicate | hasFictionalRule |
P107617
|
FINISHED |
| Object | admission by audition |
—
|
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: admission by audition | Statement: [Hollywood Arts High School, hasFictionalRule, admission by audition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalRule Context triple: [Hollywood Arts High School, hasFictionalRule, admission by audition]
-
A.
hasFictionalFunction
Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
-
B.
hasFictionalPractice
chosen
Indicates that an entity engages in, is associated with, or features a practice, activity, or procedure that exists only within a fictional or imagined context.
-
C.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
D.
hasFictionalScope
Indicates that something pertains to, applies within, or is limited to a fictional or imagined context rather than real-world scope.
-
E.
hasRulebook
Indicates that one entity possesses, is governed by, or is associated with a specific rulebook.
- 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_69e7ab3e025c819086771607157f0015 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f70e8755a48190931eaa77946f9460 |
completed | May 3, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f70abc00848190a1c3f495ef6c8dc6 |
completed | May 3, 2026, 8:43 a.m. |
Created at: April 22, 2026, 8:31 a.m.