Triple
T319596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diedrich Knickerbocker |
E7783
|
entity |
| Predicate | genreAssociation |
P14
|
FINISHED |
| Object | satire |
—
|
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: satire | Statement: [Diedrich Knickerbocker, genreAssociation, satire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreAssociation Context triple: [Diedrich Knickerbocker, genreAssociation, satire]
-
A.
knownForMusicGenre
Indicates that an entity is recognized or notable for producing, performing, or being associated with a particular music genre.
-
B.
genreFeatures
Indicates that a particular genre is characterized or defined by certain features or attributes.
-
C.
genreDiversity
Indicates the extent to which an entity involves, includes, or spans multiple distinct genres rather than being confined to a single genre.
-
D.
associatedMusic
Indicates a relationship where one entity is linked to or connected with a piece of music, such as being used by, related to, or thematically tied to that music.
-
E.
genre
chosen
Indicates the artistic or thematic category to which a work (such as a book, film, or song) belongs.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea7edbc48190b9031bd1af48f72a |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e946607081909c8b97473aaf8d1b |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.