Triple
T34548957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topolino's Terrace – Flavors of the Riviera |
E887008
|
entity |
| Predicate | characterMealName |
P200011
|
FINISHED |
| Object | Breakfast à la Art with Mickey & Friends |
—
|
NE NERFINISHED |
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: Breakfast à la Art with Mickey & Friends | Statement: [Topolino's Terrace – Flavors of the Riviera, characterMealName, Breakfast à la Art with Mickey & Friends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterMealName Context triple: [Topolino's Terrace – Flavors of the Riviera, characterMealName, Breakfast à la Art with Mickey & Friends]
-
A.
characterName
Indicates that an entity has a specific name used to identify its character.
-
B.
isFoodThemedHero
Indicates that the hero’s identity, powers, appearance, or motif is primarily based on or themed around food.
-
C.
commonMealType
Indicates that two entities share the same general category or type of meal (e.g., breakfast, lunch, dinner).
-
D.
knownForDish
Indicates that an entity is recognized or notable for preparing, serving, or being associated with a particular dish.
-
E.
characterFullName
Indicates that the predicate specifies the complete, formal name of a character.
- F. None of above. chosen
Provenance (4 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_69f349cff89081908f91e0b064f4833e |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff6a4ce9a08190b98abde3a170dd69 |
completed | May 9, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69ff69c11634819089d1084bd2c11534 |
completed | May 9, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69ff6a4c32dc819097f591944bee8851 |
completed | May 9, 2026, 5:09 p.m. |
Created at: May 1, 2026, 2:02 a.m.