Triple
T20125256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Krupp |
E490732
|
entity |
| Predicate | hasCatchphraseAsCaptainUnderpants |
P74838
|
FINISHED |
| Object | Tra-la-laaa! |
—
|
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: Tra-la-laaa! | Statement: [Mr. Krupp, hasCatchphraseAsCaptainUnderpants, Tra-la-laaa!]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCatchphraseAsCaptainUnderpants Context triple: [Mr. Krupp, hasCatchphraseAsCaptainUnderpants, Tra-la-laaa!]
-
A.
hasCatchphraseStyle
Indicates that an entity’s catchphrase conforms to, or is characterized by, a particular stylistic pattern or manner of expression.
-
B.
characterCatchphrase
chosen
Indicates that a particular phrase is commonly and distinctively used by a character as their catchphrase.
-
C.
notableCatchphraseUser
Indicates that the subject is a person who is notably associated with using a particular catchphrase.
-
D.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
E.
laterCaptain
Indicates that one entity becomes the captain of something at a later time than another referenced captain or captaincy.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667412b888190b43f7dd1ccdbad01 |
completed | April 20, 2026, 5:49 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:31 p.m.