Triple
T24313119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Temptress |
E612726
|
entity |
| Predicate | blacklistedCensorshipIssues |
P155522
|
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: [The Temptress, blacklistedCensorshipIssues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blacklistedCensorshipIssues Context triple: [The Temptress, blacklistedCensorshipIssues, yes]
-
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.
censorshipTarget
Indicates that an entity is the object or focus of censorship by another entity or authority.
-
D.
countryOfCensorshipControversy
Indicates the country in which a particular censorship-related controversy or dispute took place.
-
E.
hasCensorshipControversy
Indicates that an entity has been involved in disputes, criticism, or public debate related to censorship of its content or activities.
- 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_69e2d7da491c8190b6e6218af50923db |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292a569dc81908971a4026e612a05 |
completed | April 29, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69f1c45f45888190a9ccc225906c34bd |
completed | April 29, 2026, 8:42 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 1:44 a.m.