Triple
T37383498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scooby Snacks |
E928500
|
entity |
| Predicate | catchphraseContext |
P132994
|
FINISHED |
| Object | "Would you do it for a Scooby Snack?" |
—
|
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: "Would you do it for a Scooby Snack?" | Statement: [Scooby Snacks, catchphraseContext, "Would you do it for a Scooby Snack?"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: catchphraseContext Context triple: [Scooby Snacks, catchphraseContext, "Would you do it for a Scooby Snack?"]
-
A.
featuresCatchphrase
Indicates that an entity prominently includes or is associated with a particular catchphrase.
-
B.
typicalPhrase
chosen
Indicates that the object is a phrase commonly or characteristically used in connection with the subject.
-
C.
hasCatchphraseStyle
Indicates that an entity’s catchphrase conforms to, or is characterized by, a particular stylistic pattern or manner of expression.
-
D.
usedCatchphraseTheme
Indicates that an entity employed a particular catchphrase as a recurring thematic element or motif.
-
E.
usedPhrase
Indicates that one entity employed or expressed a particular phrase in speech, writing, or another form of communication.
- 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_69f76eb9e66881908534cf22d04c3b5a |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb9e1845e881908d19158440cf3b87 |
completed | May 6, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69fb8d08d6988190a00794ac26078348 |
completed | May 6, 2026, 6:48 p.m. |
Created at: May 3, 2026, 4:16 p.m.