Triple
T9320805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A1 motorway |
E224249
|
entity |
| Predicate | connectsBorderArea |
P28958
|
FINISHED |
| Object | French border near Geneva |
—
|
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 border near Geneva | Statement: [A1 motorway, connectsBorderArea, French border near Geneva]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsBorderArea Context triple: [A1 motorway, connectsBorderArea, French border near Geneva]
-
A.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
B.
hasBorderConnection
Indicates that two regions or entities share a common boundary or are directly connected along a border.
-
C.
connectsCountryBorder
chosen
Indicates that one entity forms a direct land or maritime border connection with a specified country.
-
D.
relatedBorder
Indicates that two geographic or political entities share a common boundary or border with each other.
-
E.
borderConnectionPlanned
Indicates that a border crossing or boundary link between two regions is intended or scheduled to be established in the future.
- 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_69ca8426d48481909596360f7791c7dd |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd358dcb4c81909e00bfb58a6dda3f |
completed | April 1, 2026, 3:11 p.m. |
| PD | Predicate disambiguation | batch_69cc7a61e9a4819096eb014f3791ef2e |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:38 p.m.