Triple
T14359610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Hader as Stefon Zolesky |
E356061
|
entity |
| Predicate | knownForCatchphrases |
P74838
|
FINISHED |
| Object | idiosyncratic club descriptions |
—
|
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: idiosyncratic club descriptions | Statement: [Bill Hader as Stefon Zolesky, knownForCatchphrases, idiosyncratic club descriptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownForCatchphrases Context triple: [Bill Hader as Stefon Zolesky, knownForCatchphrases, idiosyncratic club descriptions]
-
A.
characterCatchphrase
chosen
Indicates that a particular phrase is commonly and distinctively used by a character as their catchphrase.
-
B.
hasCatchphraseStyle
Indicates that an entity’s catchphrase conforms to, or is characterized by, a particular stylistic pattern or manner of expression.
-
C.
featuresCatchphrase
Indicates that an entity prominently includes or is associated with a particular catchphrase.
-
D.
namedForKnownFor
Indicates that one entity is named after another entity specifically because that other entity is notable or recognized for something.
-
E.
knownForStoryline
Indicates that an entity is recognized or notable specifically for its narrative or storyline.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f54bfb08190a27c0d12731acec2 |
completed | April 14, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69de2a9958e881909d03ac03f135163e |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:15 a.m.