Triple
T28984167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France–Belgium border |
E734632
|
entity |
| Predicate | hasLanguageZone |
P3950
|
FINISHED |
| Object | French-speaking areas |
—
|
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-speaking areas | Statement: [France–Belgium border, hasLanguageZone, French-speaking areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageZone Context triple: [France–Belgium border, hasLanguageZone, French-speaking areas]
-
A.
languageZone
chosen
Indicates the linguistic region or area in which a language is predominantly used or officially recognized.
-
B.
hasTimeZones
Indicates that an entity is associated with one or more time zones in which it is valid or operates.
-
C.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
-
D.
hasLanguageRegionContext
Indicates that something is associated with or situated within a specific linguistic or language-region context.
-
E.
hasNumberOfNationalTimeZones
Indicates the quantity of distinct official time zones that a nation or country uses within its territory.
- 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_69f05b0dd9b481908b7901e1c95ff6b2 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fd231cab588190ad0953dc8f4af8f2 |
completed | May 7, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69fd1aa3f1c481909fe6e9cab1383551 |
completed | May 7, 2026, 11:05 p.m. |
Created at: April 28, 2026, 9:13 a.m.