Triple
T30690840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mathis der Maler |
E781321
|
entity |
| Predicate | censorshipStatusInNaziGermany |
P40618
|
FINISHED |
| Object | banned |
—
|
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: banned | Statement: [Mathis der Maler, censorshipStatusInNaziGermany, banned]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: censorshipStatusInNaziGermany Context triple: [Mathis der Maler, censorshipStatusInNaziGermany, banned]
-
A.
legalStatusInNaziView
Indicates the legal classification or standing an entity was assigned under Nazi ideology and law.
-
B.
countryDuringNaziPeriod
Indicates that an entity functioned as a country or national state specifically during the historical period of Nazi rule.
-
C.
censorshipStatusAtTime
chosen
Indicates the censorship status of something at a specific point in time, capturing whether and how it was censored then.
-
D.
censorshipLevel
Indicates the degree or strictness of control, suppression, or restriction applied to information, media, or expression.
-
E.
typeOfCensorship
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_69f224ab24e08190991d6edb6df58e8b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68b8875ac8190bcd509983fb51b01 |
completed | May 2, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69f6861170d08190bb98be609d436f84 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:33 p.m.