Triple
T20598779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disney Legend |
E506117
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object | Mickey Mouse |
—
|
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: Mickey Mouse | Statement: [Disney Legend, notableRecipient, Mickey Mouse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mickey Mouse Context triple: [Disney Legend, notableRecipient, Mickey Mouse]
-
A.
Mickey Mouse
chosen
Mickey Mouse is an iconic cartoon character created by Walt Disney, widely recognized as the cheerful mascot of The Walt Disney Company and a symbol of global popular culture.
-
B.
Mickey
Mickey is the nickname of Gordon "Mickey" Cochrane, a Hall of Fame American Major League Baseball catcher and manager from the early 20th century.
-
C.
Mickey
Mickey is the central protagonist of the 1938 horse-racing drama film "Stablemates," around whom the story’s emotional and narrative arc revolves.
-
D.
Mickey
Mickey is the nickname of English actress Mickey Sumner, known for her roles in film and television such as "Frances Ha" and "Snowpiercer."
-
E.
Mickey
Mickey is a common diminutive form of the given name Michael, often used as a familiar or informal first name.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa1e251c8190926dafe1402eb63c |
completed | April 20, 2026, 10:35 p.m. |
Created at: April 16, 2026, 11:40 a.m.