Triple
T33732508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münster im Elsass |
E864308
|
entity |
| Predicate | gehörtZurSprachregion |
P53840
|
FINISHED |
| Object | deutsch-französischer Grenzraum |
—
|
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: deutsch-französischer Grenzraum | Statement: [Münster im Elsass, gehörtZurSprachregion, deutsch-französischer Grenzraum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gehörtZurSprachregion Context triple: [Münster im Elsass, gehörtZurSprachregion, deutsch-französischer Grenzraum]
-
A.
regionOfMajorLanguage
Indicates the geographic region where a particular language is predominantly spoken or holds major usage.
-
B.
ethnoLinguisticRegionOf
Indicates that a region is defined or characterized by the shared ethnic and linguistic identity of the group associated with it.
-
C.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
D.
alsoInLanguageRegion
chosen
Indicates that two or more entities are located within or associated with the same language-defined geographic region.
-
E.
formerLanguageRegion
Indicates that a region previously used a particular language as significant or dominant, but no longer does so.
- 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_69f3498a64cc8190b4b414c67b280d93 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb1fcda08190a503098914ba09ab |
completed | May 3, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69f6f96dd4c8819093d6a7bd046a9ad5 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:44 a.m.