Triple
T9936096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Union fait la Force |
E192762
|
entity |
| Predicate | variantLanguage |
P64249
|
FINISHED |
| Object | Dutch |
—
|
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: Dutch | Statement: [L’Union fait la Force, variantLanguage, Dutch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: variantLanguage Context triple: [L’Union fait la Force, variantLanguage, Dutch]
-
A.
languageVariant
Indicates that one language is a variant, dialect, or localized form of another language.
-
B.
linguisticVariant
Indicates that one linguistic form is an alternative version or expression of another within the same or closely related language context.
-
C.
languageAffected
Indicates that one entity has an impact on, modifies, or influences the characteristics, usage, or status of a language.
-
D.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
E.
languageOfVariant
chosen
Indicates that one entity is the language in which a particular variant or version of another entity is expressed.
- 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_69ca82dd978c8190947124ab0d3315ac |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e3bfa88190a88f20f2687a2583 |
completed | April 2, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9428cc81909b4b4938566d78a7 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:44 p.m.