Triple
T16541621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German universities of applied sciences |
E401831
|
entity |
| Predicate | typicalProgramType |
P2192
|
FINISHED |
| Object | bachelor programmes |
—
|
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: bachelor programmes | Statement: [German universities of applied sciences, typicalProgramType, bachelor programmes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProgramType Context triple: [German universities of applied sciences, typicalProgramType, bachelor programmes]
-
A.
programType
chosen
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
B.
notableProgramType
Indicates that the subject is recognized for or associated with a particular type or category of program.
-
C.
targetAudienceOfPrograms
Indicates that a particular group or entity is the intended audience or recipient of certain programs.
-
D.
typicalBroadcastFormat
Indicates the usual or standard broadcast format in which something (such as a program or content) is typically transmitted.
-
E.
typicalProgramElement
Indicates that one program element is a representative or characteristic example of another program element or category of elements.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3455cf4b88190b3c9e93e158a7686 |
completed | April 18, 2026, 8:48 a.m. |
| PD | Predicate disambiguation | batch_69e2969fab208190ad64164d24748c45 |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:15 a.m.