Triple
T10831263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topolino's Terrace character breakfast |
E255623
|
entity |
| Predicate | characterCostumesTheme |
P90245
|
FINISHED |
| Object | European-inspired outfits |
—
|
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: European-inspired outfits | Statement: [Topolino's Terrace character breakfast, characterCostumesTheme, European-inspired outfits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterCostumesTheme Context triple: [Topolino's Terrace character breakfast, characterCostumesTheme, European-inspired outfits]
-
A.
themeInvolvingCharacter
Indicates that a theme, motif, or abstract concept centrally involves or is significantly shaped by a particular character.
-
B.
colorAssociatedWithCostume
Indicates that a particular color is thematically or typically linked to a specific costume.
-
C.
costumeDesignEmphasisOn
chosen
Indicates that a costume design places particular focus or priority on a specified element, style, feature, or thematic aspect.
-
D.
costumeType
Indicates the specific kind or category of costume associated with an entity.
-
E.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d744222288819093258b452569acab |
completed | April 9, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69d70d25280c8190b648d7d1958b413a |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:19 p.m.