Triple
T38380461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vexin |
E893744
|
entity |
| Predicate | currentRegionLanguage |
P10892
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Vexin, currentRegionLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentRegionLanguage Context triple: [Vexin, currentRegionLanguage, French]
-
A.
subjectLanguageRegion
Indicates that the subject is associated with or uses a language specific to a particular geographic region.
-
B.
regionLanguage
chosen
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
C.
operatorLanguageRegion
Indicates the geographic region or locale in which an operator’s language is used or applicable.
-
D.
currentLanguageSituation
Indicates the language currently being used or in effect in a given context or situation.
-
E.
localLanguageStatus
Indicates the status or condition of a language as used within a specific local or regional context.
- 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_69f76e4b1f748190a380696a16eae4a2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffe01c9d3c819084c256bb3c81c0dc |
completed | May 10, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69ffdfcc78b08190aa4493f13d62a531 |
completed | May 10, 2026, 1:30 a.m. |
Created at: May 3, 2026, 4:31 p.m.