Triple
T32363205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heated |
E826919
|
entity |
| Predicate | featuresAfrobeatsInfluence |
P202919
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Heated, featuresAfrobeatsInfluence, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresAfrobeatsInfluence Context triple: [Heated, featuresAfrobeatsInfluence, yes]
-
A.
featuresR&BInfluence
Indicates that the subject incorporates or is characterized by stylistic or musical elements typical of R&B.
-
B.
featuresHipHopInfluence
Indicates that something incorporates stylistic, rhythmic, or cultural elements characteristic of hip hop.
-
C.
hasRhythmInfluence
Indicates that one entity has influenced or shaped the rhythmic patterns, style, or timing characteristics of another entity.
-
D.
countryInfluenced
Indicates that one country has exerted a significant political, economic, cultural, or military impact on another country.
-
E.
influenceOnGenre
Indicates how strongly one entity has shaped, affected, or contributed to the development or characteristics of a particular genre.
- F. None of above. chosen
Provenance (4 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_69f349166d548190887b412fe908e2f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a00d08e8fac8190b59359134e6e1c03 |
completed | May 10, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_6a00d0127c088190a6f5b360450af113 |
completed | May 10, 2026, 6:36 p.m. |
| PDg | Predicate description generation | batch_6a00d08de20881908d83d98362e0751c |
completed | May 10, 2026, 6:38 p.m. |
Created at: May 1, 2026, 12:50 a.m.