Triple
T32042081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Merida |
E818242
|
entity |
| Predicate | portrayedInThemeParksBy |
P196155
|
FINISHED |
| Object | face character performers at Disney Parks |
—
|
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: face character performers at Disney Parks | Statement: [Princess Merida, portrayedInThemeParksBy, face character performers at Disney Parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedInThemeParksBy Context triple: [Princess Merida, portrayedInThemeParksBy, face character performers at Disney Parks]
-
A.
hasThemePark
Indicates that one entity owns, contains, or is associated with a theme park as part of its properties or offerings.
-
B.
themeParkMascotFor
Indicates that one entity serves as the official mascot character representing a particular theme park.
-
C.
locatedInThemeParkType
Indicates that an entity is situated within or belongs to a specific type or category of theme park.
-
D.
portrayedInAnimationBy
Indicates that an entity is represented or voiced in an animated work by a particular performer or creator.
-
E.
hasAnimatronicCharacter
Indicates that an entity features, includes, or is associated with an animatronic 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_69f348fcfb648190859f6be5e04b7cfe |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe0d165a48819098b854318a50d76c |
completed | May 8, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69fe0931002481908a95b34f95e9f64e |
completed | May 8, 2026, 4:02 p.m. |
| PDg | Predicate description generation | batch_69fe0d14778c8190986fa4f37f992a2f |
completed | May 8, 2026, 4:19 p.m. |
Created at: May 1, 2026, 12:19 a.m.