Triple
T38120056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Totally Fucked |
E951907
|
entity |
| Predicate | workTypeWithinShow |
P115247
|
FINISHED |
| Object | 11 o’clock number–style outburst |
—
|
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: 11 o’clock number–style outburst | Statement: [Totally Fucked, workTypeWithinShow, 11 o’clock number–style outburst]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workTypeWithinShow Context triple: [Totally Fucked, workTypeWithinShow, 11 o’clock number–style outburst]
-
A.
worksOnShowWithinShow
Indicates that one entity works on a specific show that exists as a sub-show or segment within a larger overarching show.
-
B.
worksOnTVShow
Indicates that an individual is professionally involved in the production or creation of a particular television show.
-
C.
portrayedInWorkType
chosen
Indicates that an entity is depicted or represented within a work of a specified type (such as a film, book, painting, or other creative medium).
-
D.
workChronologyWithinShow
Indicates the chronological ordering of work-related events or contributions as they occur within the timeline of a specific show.
-
E.
workFilmType
Indicates the type or category of film associated with a particular work.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:21 p.m.