Triple
T3126019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crack Music |
E65297
|
entity |
| Predicate | hasPoliticalContent |
P40887
|
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: [Crack Music, hasPoliticalContent, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPoliticalContent Context triple: [Crack Music, hasPoliticalContent, true]
-
A.
politicallySensitive
chosen
Indicates that an entity, action, or topic is associated with political issues, controversies, or power structures such that it may require special caution, regulation, or discretion.
-
B.
hasPoliticalCharacteristic
Indicates that an entity possesses a specific political attribute, quality, or affiliation.
-
C.
hasPoliticalDialogue
Indicates that there is an exchange or interaction between entities involving political topics, issues, or negotiations.
-
D.
politicalRepercussion
Indicates that an action, event, or decision leads to consequences or fallout within a political context, such as shifts in power, public opinion, or policy.
-
E.
hasPoliticalComposition
Indicates that an entity has a particular political makeup or distribution of political affiliations, parties, or ideologies.
- 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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada52e9f348190aa15b2c0d252719f |
completed | March 8, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69ad9df62e548190b053e1478deed467 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:04 p.m.