Triple
T9430111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Primetime Emmy Award for Outstanding Writing for a Variety Series |
E227352
|
entity |
| Predicate | notableGenreExamples |
P55821
|
FINISHED |
| Object | sketch comedy shows |
—
|
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: sketch comedy shows | Statement: [Primetime Emmy Award for Outstanding Writing for a Variety Series, notableGenreExamples, sketch comedy shows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableGenreExamples Context triple: [Primetime Emmy Award for Outstanding Writing for a Variety Series, notableGenreExamples, sketch comedy shows]
-
A.
commonGenre
Indicates that two entities share at least one genre in common.
-
B.
notableWorkGenre
Indicates that a particular work is recognized as notable for an entity and specifies the genre to which that work belongs.
-
C.
seriesGenreOfNotableWork
Indicates that a particular genre characterizes the notable work associated with a series.
-
D.
typicalGenresIncluded
chosen
Indicates that certain genres are commonly or characteristically included as part of another entity’s usual set of genres.
-
E.
keyGenreFilm
Indicates that a particular genre is the primary or defining genre associated with a given film.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7c94719c81909d7743a57c45e07f |
completed | April 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69cca55548488190b171ae695a3212de |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:49 p.m.