Triple
T21921333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Castellaneta |
E541322
|
entity |
| Predicate | voiceRole |
P12691
|
FINISHED |
| Object | Mayor Quimby |
—
|
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: Mayor Quimby | Statement: [Dan Castellaneta, voiceRole, Mayor Quimby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayor Quimby Context triple: [Dan Castellaneta, voiceRole, Mayor Quimby]
-
A.
Mayor Quimby
chosen
Mayor Quimby is a fictional, Kennedy-esque, often corrupt mayor of Springfield on the animated television series "The Simpsons."
-
B.
Mayor Lovett
Mayor Lovett is a fictional political figure who serves as the central character in the story "Meet John Doe."
-
C.
Mayor Kline
Mayor Kline is a fictional small-town politician and mayor from the television series "Stranger Things."
-
D.
Mayor Tortoise John
Mayor Tortoise John is the scheming, wheelchair-bound tortoise who serves as the main antagonist and corrupt mayor in the animated Western film "Rango."
-
E.
Mayor McGerkle
Mayor McGerkle is a cheerful, well-meaning civic leader in the 2018 animated film "The Grinch," serving as the enthusiastic mayor of Whoville.
- 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_69e0c47d74488190a15119108794a307 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1233c29008190b84ae551b14eb2db |
completed | April 28, 2026, 9:14 p.m. |
Created at: April 16, 2026, 7:45 p.m.