Triple
T2571337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afro-Latin Americans |
E57670
|
entity |
| Predicate | musicGenreInfluenced |
P20936
|
FINISHED |
| Object | samba |
—
|
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: samba | Statement: [Afro-Latin Americans, musicGenreInfluenced, samba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: musicGenreInfluenced Context triple: [Afro-Latin Americans, musicGenreInfluenced, samba]
-
A.
hasGenreInfluenceOn
Indicates that one genre has a notable impact on shaping or influencing the characteristics, style, or development of another genre.
-
B.
influencedByGenre
chosen
Indicates that something’s characteristics, style, or development are shaped or affected by a particular genre.
-
C.
styleOfMusic
Indicates the musical genre or stylistic category that characterizes a piece of music, artist, or performance.
-
D.
influencedArtist
Indicates that one artist has had a significant impact on the style, work, or development of another artist.
-
E.
musicGenreBroad
Indicates that one music genre is a broader, more general category that encompasses another, more specific music genre.
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd383dce881909411a38c6d37bc3a |
completed | March 7, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69abd0ce4dcc8190b17a65abf9bd1bb0 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:48 p.m.