Triple
T27957426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landesminister |
E703588
|
entity |
| Predicate | istÜbergeordnetBegriffVon |
P2372
|
FINISHED |
| Object | nordrhein-westfälischer Minister |
—
|
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: nordrhein-westfälischer Minister | Statement: [Landesminister, istÜbergeordnetBegriffVon, nordrhein-westfälischer Minister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: istÜbergeordnetBegriffVon Context triple: [Landesminister, istÜbergeordnetBegriffVon, nordrhein-westfälischer Minister]
-
A.
classificationTerm
Indicates that one entity serves as a categorical label or type used to classify or group another entity.
-
B.
generalizationOf
chosen
Indicates that one entity represents a broader, more general concept or category that subsumes or abstracts over another, more specific entity.
-
C.
basingConcept
Indicates that one concept serves as the foundational basis or underlying rationale for another concept.
-
D.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
E.
definitionOfRelatedConcept
Indicates that one concept provides the formal meaning, explanation, or characterization of another closely related concept.
- 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_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6895f0819088655277e45859a8 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 7:29 p.m.