Triple
T23954015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomainia |
E603727
|
entity |
| Predicate | hasCensorshipTheme |
P133958
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tomainia, hasCensorshipTheme, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCensorshipTheme Context triple: [Tomainia, hasCensorshipTheme, yes]
-
A.
hasCensorshipControversy
chosen
Indicates that an entity has been involved in disputes, criticism, or public debate related to censorship of its content or activities.
-
B.
wasCensored
Indicates that an entity’s content, expression, or communication was suppressed, altered, or restricted by an authority or controlling party.
-
C.
censoredSetting
Indicates that a setting or environment has been modified to restrict, remove, or obscure certain content or information.
-
D.
typeOfCensorship
Indicates the specific kind or method of censorship being applied in a given context.
-
E.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
- 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_69e2954222288190a7323554d0cca8d7 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d0d558748190a51b5732a3e6713c |
completed | April 29, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69f1615518088190a206f54e2fdb14a3 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:21 p.m.