Triple
T13101538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mickey & Friends Parking Structure |
E310729
|
entity |
| Predicate | hasSection |
P35
|
FINISHED |
| Object |
Donald
Donald is a themed parking section at Disneyland’s Mickey & Friends Parking Structure, named after the Disney character Donald Duck.
|
E1021429
|
NE FINISHED |
How this triple was built (4 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: Donald | Statement: [Mickey & Friends Parking Structure, hasSection, Donald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donald Context triple: [Mickey & Friends Parking Structure, hasSection, Donald]
-
A.
Donald
Donald is a fictional character portrayed by American actor Matt Bomer, likely in a film or television production.
-
B.
Donald
Donald is the given first name of American actor Don Cheadle, known for his acclaimed film and television roles.
-
C.
Donald
Donald is the given name of Donald W. Riegle Jr., a former United States Senator from Michigan.
-
D.
Donald
Donald is the given first name of Don Drysdale, the Hall of Fame Major League Baseball pitcher known for his dominant career with the Los Angeles Dodgers.
-
E.
Donald
Donald is the given first name of Don Gordon, an American actor known for his supporting roles in film and television during the mid-20th century.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Donald Triple: [Mickey & Friends Parking Structure, hasSection, Donald]
Generated description
Donald is a themed parking section at Disneyland’s Mickey & Friends Parking Structure, named after the Disney character Donald Duck.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Donald Target entity description: Donald is a themed parking section at Disneyland’s Mickey & Friends Parking Structure, named after the Disney character Donald Duck.
-
A.
Donald
Donald is the given name of Don Bluth, the renowned American animator and film director known for works like "The Secret of NIMH" and "An American Tail."
-
B.
Donald
Donald is the given name of Donald Deskey, an influential American industrial designer best known for his work on Radio City Music Hall’s interiors.
-
C.
Donald
Donald is the given first name of Don Drysdale, the Hall of Fame Major League Baseball pitcher known for his dominant career with the Los Angeles Dodgers.
-
D.
Donald
Donald is the given name of Donald Trump, the 45th president of the United States and a prominent businessman and media personality.
-
E.
Donald
Donald is the first name of American comedian and actor Don Rickles, famed for his pioneering insult comedy style.
- F. None of above. chosen
Provenance (5 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981515d488190908d3cca1b84a42d |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e275df6c819096bb59e64df35216 |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e32bf5508190b4dc58971f8f64d0 |
completed | May 3, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e407dd988190b928b8931985a815 |
completed | May 3, 2026, 5:58 a.m. |
Created at: April 9, 2026, 9:04 p.m.