Triple
T33732476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münster im Elsass |
E864308
|
entity |
| Predicate | hatRegionaleSprache |
P1762
|
FINISHED |
| Object | Elsässisch |
—
|
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: Elsässisch | Statement: [Münster im Elsass, hatRegionaleSprache, Elsässisch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hatRegionaleSprache Context triple: [Münster im Elsass, hatRegionaleSprache, Elsässisch]
-
A.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
B.
spokenInCanton
Indicates that something (typically a language, phrase, or communication) is expressed using the Cantonese dialect.
-
C.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
D.
regionalDialect
chosen
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
-
E.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
- 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.