Triple
T23227199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Un ballo in maschera |
E581045
|
entity |
| Predicate | censorshipConsequence |
P151434
|
FINISHED |
| Object | change of setting from Sweden to Boston |
—
|
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: change of setting from Sweden to Boston | Statement: [Un ballo in maschera, censorshipConsequence, change of setting from Sweden to Boston]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: censorshipConsequence Context triple: [Un ballo in maschera, censorshipConsequence, change of setting from Sweden to Boston]
-
A.
censorshipIssues
Indicates that one entity imposes restrictions, suppression, or control over the information, expression, or content associated with another entity.
-
B.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
C.
typeOfCensorship
Indicates the specific kind or method of censorship being applied in a given context.
-
D.
censorshipLevel
Indicates the degree or strictness of control, suppression, or restriction applied to information, media, or expression.
-
E.
censorshipTarget
Indicates that an entity is the object or focus of censorship by another entity or authority.
- 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_69e246043c48819089bae72c9a9c306c |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f1922f5b4081908145d66ea7534493 |
completed | April 29, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69effcdadec0819092ec1749ee453b4e |
completed | April 28, 2026, 12:18 a.m. |
| PDg | Predicate description generation | batch_69f01d8770d081908897c28b04e5faea |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 17, 2026, 4:09 p.m.