Triple
T28920815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regionalverband Saarbrücken |
E733505
|
entity |
| Predicate | hasGermanMunicipalityKey |
P180512
|
FINISHED |
| Object | 10041 |
—
|
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: 10041 | Statement: [Regionalverband Saarbrücken, hasGermanMunicipalityKey, 10041]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGermanMunicipalityKey Context triple: [Regionalverband Saarbrücken, hasGermanMunicipalityKey, 10041]
-
A.
isBavarianMunicipality
Indicates that an entity functions as a municipality located within the federal state of Bavaria.
-
B.
hasLandkreis
Indicates that an entity is associated with, or belongs to, a specific administrative district (Landkreis).
-
C.
federalStateOfGermany
Indicates that one entity is a federal state (Bundesland) that is a constituent state within the country of Germany.
-
D.
hasMunicipalAreaRankingInGermany
Indicates that a municipality holds a specific rank or position in comparison to other municipalities in Germany based on its area size.
-
E.
containsGermanSpeakingArea
Indicates that one entity geographically includes an area where German is predominantly spoken.
- 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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69f7431aac148190bb6aac59817c174a |
completed | May 3, 2026, 12:44 p.m. |
Created at: April 28, 2026, 8:19 a.m.