Triple
T35839181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont Salève |
E1036025
|
entity |
| Predicate | administrativeBorderNearby |
P120977
|
FINISHED |
| Object | France–Switzerland border |
—
|
NE NERFINISHED |
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: France–Switzerland border | Statement: [Mont Salève, administrativeBorderNearby, France–Switzerland border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeBorderNearby Context triple: [Mont Salève, administrativeBorderNearby, France–Switzerland border]
-
A.
borderingLocalGovernmentArea
Indicates that one local government area shares a common boundary with another local government area.
-
B.
countyBorder
Indicates that two counties share a common boundary or border with each other.
-
C.
countryBorderProximity
Indicates that one country is geographically close to or directly bordering another country.
-
D.
borderingAdministrativeTerritorialEntity
chosen
Indicates that one administrative territorial entity shares a common boundary with another administrative territorial entity.
-
E.
borderingCountryNearby
Indicates that one country is geographically close to, but does not necessarily share a direct land border with, another country.
- 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_69f76e1a29e8819088280f26096aeb55 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
Created at: May 3, 2026, 4:06 p.m.