Triple
T19421414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franekeraner |
E485862
|
entity |
| Predicate | hasDemonymLanguageForm |
P81270
|
FINISHED |
| Object | German language |
—
|
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: German language | Statement: [Franekeraner, hasDemonymLanguageForm, German language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDemonymLanguageForm Context triple: [Franekeraner, hasDemonymLanguageForm, German language]
-
A.
hasDemonymLanguage
chosen
Indicates that a language is used as the demonym (people’s name or adjective of nationality) for inhabitants of a particular place or group.
-
B.
hasDemonym
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
C.
usesDemonymForm
Indicates that one entity refers to another using a demonym form, i.e., a name derived from the inhabitants or nationality associated with that entity.
-
D.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
E.
relatedDemonym
Indicates that one entity is the demonym (name for residents or natives) associated with the other entity.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63214d768819082129100d7116521 |
completed | April 20, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69e4fd68b1f881908d273de1fee81a75 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:37 p.m.