Triple
T3456899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Chapman Report |
E72924
|
entity |
| Predicate | censorshipIssues |
P48338
|
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 Chapman Report, censorshipIssues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: censorshipIssues Context triple: [The Chapman Report, censorshipIssues, yes]
-
A.
censorshipReason
Indicates the justification or cause given for why certain content is suppressed, restricted, or removed.
-
B.
networkCensorshipIssues
Indicates that there are problems or restrictions in accessing content or services due to censorship imposed on a network.
-
C.
revisedVersionCensorshipStatus
Indicates the censorship or restriction status applied to a revised version of some original content.
-
D.
censorshipStatusAtTime
Indicates the censorship status of something at a specific point in time, capturing whether and how it was censored then.
-
E.
censorshipYear
Indicates the year in which an act of censorship was imposed on the referenced content or entity.
- 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_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbaa9837c8190aafd618c6af3446e |
completed | March 8, 2026, 6:06 p.m. |
| PD | Predicate disambiguation | batch_69adae041d588190a84a02bca94adec8 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaed74ecc8190b74dc70ab59a3e1c |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:16 p.m.