Triple
T21041700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willkommen |
E518340
|
entity |
| Predicate | mixesLanguages |
P75592
|
FINISHED |
| Object | German, French, and English |
—
|
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, French, and English | Statement: [Willkommen, mixesLanguages, German, French, and English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mixesLanguages Context triple: [Willkommen, mixesLanguages, German, French, and English]
-
A.
languageMix
chosen
Indicates that multiple languages are used together or intermixed within the same context, communication, or content.
-
B.
hasVocalLanguageMix
Indicates that an entity’s vocal communication combines multiple languages or language varieties within its speech.
-
C.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
D.
languageOnBothSides
Indicates that the same language is used or present on both sides of a given relationship, boundary, or comparison.
-
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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf0b27881909d1c5b58be387a74 |
completed | April 21, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:15 p.m.