Triple
T95152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France–Switzerland border |
E1913
|
entity |
| Predicate | languageZone |
P3950
|
FINISHED |
| Object | French-speaking regions on both sides |
—
|
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 regions on both sides | Statement: [France–Switzerland border, languageZone, French-speaking regions on both sides]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageZone Context triple: [France–Switzerland border, languageZone, French-speaking regions on both sides]
-
A.
locale
Indicates that one entity is the place, setting, or geographic area in which another entity exists, occurs, or is situated.
-
B.
languageBranch
Indicates that one language belongs to, or is classified under, a broader linguistic branch or subgroup.
-
C.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
D.
languageOfWorkOrName
Indicates the language in which a work is created or a name is expressed.
-
E.
languageFamily
Indicates that two or more languages belong to the same genealogical language family or linguistic lineage.
- F. None of above. chosen
Provenance (4 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24feef1b08190bb9525f71cce053e |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebb3da08190a8b82564f33cde3b |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a24fed6b8c819080a6c0cd3b16e6bd |
completed | Feb. 28, 2026, 2:16 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.