Triple
T8176409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miami bass |
E190945
|
entity |
| Predicate | legalAndSocialImpact |
P4312
|
FINISHED |
| Object | involved in U.S. obscenity controversies |
—
|
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: involved in U.S. obscenity controversies | Statement: [Miami bass, legalAndSocialImpact, involved in U.S. obscenity controversies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalAndSocialImpact Context triple: [Miami bass, legalAndSocialImpact, involved in U.S. obscenity controversies]
-
A.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
B.
socialImpact
chosen
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
C.
legislativeImpact
Indicates the effect that a law or legislative action has on a policy, entity, or outcome.
-
D.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
E.
regulationImpact
Indicates how a regulation influences, constrains, or alters the behavior, performance, or outcomes associated with the related entities.
- 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4ab8295081909a450fcaa34f6ec6 |
completed | March 31, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69cb36a7952481908f34e3e82f375a84 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:40 p.m.