Triple
T6521348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A King in New York |
E151188
|
entity |
| Predicate | censorshipStatusInUSA |
P40618
|
FINISHED |
| Object | initially limited release |
—
|
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: initially limited release | Statement: [A King in New York, censorshipStatusInUSA, initially limited release]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: censorshipStatusInUSA Context triple: [A King in New York, censorshipStatusInUSA, initially limited release]
-
A.
censorshipLevel
Indicates the degree or strictness of control, suppression, or restriction applied to information, media, or expression.
-
B.
censorshipStatusAtTime
chosen
Indicates the censorship status of something at a specific point in time, capturing whether and how it was censored then.
-
C.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
D.
censorshipIssues
Indicates that one entity imposes restrictions, suppression, or control over the information, expression, or content associated with another entity.
-
E.
revisedVersionCensorshipStatus
Indicates the censorship or restriction status applied to a revised version of some original content.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad9431f081909b14b3df3414a55f |
completed | March 27, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69c68abbc7148190a8270d47fe10cc31 |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:45 p.m.