Triple
T7523046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mickey, Donald, Goofy: The Three Musketeers |
E177819
|
entity |
| Predicate | featuresCharacterAsRole |
P23263
|
FINISHED |
| Object | Mickey Mouse as musketeer |
—
|
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: Mickey Mouse as musketeer | Statement: [Mickey, Donald, Goofy: The Three Musketeers, featuresCharacterAsRole, Mickey Mouse as musketeer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterAsRole Context triple: [Mickey, Donald, Goofy: The Three Musketeers, featuresCharacterAsRole, Mickey Mouse as musketeer]
-
A.
featuresCharacterRole
chosen
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
B.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
C.
roleCharacteristic
Indicates that a particular characteristic, quality, or attribute is associated with and helps define a given role or function.
-
D.
characterFutureRole
Indicates the role or position that a character is expected or intended to assume at a later point in time.
-
E.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
- 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_69c69f29bf3081909a146aec7755f185 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f7c4f32081908b5162f4551adb6d |
completed | March 27, 2026, 9:33 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d6bb808190bdd04499fd3bceb6 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:46 p.m.