Triple
T35276040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Break from Toronto |
E1018805
|
entity |
| Predicate | hasChoppedAndScrewedVocals |
P121063
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Break from Toronto, hasChoppedAndScrewedVocals, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChoppedAndScrewedVocals Context triple: [Break from Toronto, hasChoppedAndScrewedVocals, true]
-
A.
hasChoppedAndScrewed
chosen
Indicates that one entity is a version of another that has been remixed using the "chopped and screwed" technique (slowed tempo and cut/repeated segments).
-
B.
hasVocals
Indicates that the subject includes or features vocal elements, such as singing or spoken voice, rather than being purely instrumental or non-vocal.
-
C.
hasBackwardVocals
Indicates that the subject uses or contains backward (reversed) vocal audio in relation to the object.
-
D.
hasAdditionalVocalElements
Indicates that an entity includes extra vocal components or embellishments beyond its primary or standard vocal parts.
-
E.
includesScratchVocalsFrom
Indicates that one audio recording or track contains scratch (temporary or demo) vocal parts taken from another recording or session.
- 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_69f76de5c4788190896ad598ae7d6bc6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69feced53a7c819098ec474fb7d514b0 |
completed | May 9, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69fecd9cd5288190aac8b4e04a7ee78e |
completed | May 9, 2026, 6:01 a.m. |
Created at: May 3, 2026, 4:02 p.m.