Triple
T38120046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Totally Fucked |
E951907
|
entity |
| Predicate | censorshipConcern |
P142663
|
FINISHED |
| Object | broadcast language restrictions |
—
|
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: broadcast language restrictions | Statement: [Totally Fucked, censorshipConcern, broadcast language restrictions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: censorshipConcern Context triple: [Totally Fucked, censorshipConcern, broadcast language restrictions]
-
A.
censorshipIssues
Indicates that one entity imposes restrictions, suppression, or control over the information, expression, or content associated with another entity.
-
B.
censorshipConsequence
Indicates the outcome or impact that results from an act or policy of censorship being applied.
-
C.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
D.
censorshipLevel
Indicates the degree or strictness of control, suppression, or restriction applied to information, media, or expression.
-
E.
typeOfCensorship
chosen
Indicates the specific kind or method of censorship being applied in a given context.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:21 p.m.