Triple
T27957421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landesminister |
E703588
|
entity |
| Predicate | istTypischFür |
P12230
|
FINISHED |
| Object | föderales Regierungssystem Deutschlands |
—
|
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: föderales Regierungssystem Deutschlands | Statement: [Landesminister, istTypischFür, föderales Regierungssystem Deutschlands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: istTypischFür Context triple: [Landesminister, istTypischFür, föderales Regierungssystem Deutschlands]
-
A.
typicalIn
chosen
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
B.
wirdGefiertAls
Indicates that someone or something is celebrated, honored, or acclaimed as having a particular role, status, or quality.
-
C.
isTypicallyServedFor
Indicates that one item is most commonly or customarily served as a meal or course for the other (e.g., a dish typically served for breakfast, lunch, or dinner).
-
D.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
E.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
- 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_69ef840c8b2c8190946ae9522774ba51 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f63b00473c8190b718fe3d0a717e32 |
completed | May 2, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69f63710d17c819084cfe96e6df334fd |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 7:29 p.m.