Triple
T22637199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A10 motorway |
E558711
|
entity |
| Predicate | nearbyAccess |
P98808
|
FINISHED |
| Object | provides access to cities in southwestern France |
—
|
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: provides access to cities in southwestern France | Statement: [A10 motorway, nearbyAccess, provides access to cities in southwestern France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyAccess Context triple: [A10 motorway, nearbyAccess, provides access to cities in southwestern France]
-
A.
hasNearbyAccess
chosen
Indicates that one entity has convenient, close-proximity access to another resource, service, or location.
-
B.
nearPass
Indicates that one entity moves or travels close to another entity without necessarily making direct contact or interaction.
-
C.
nearbyUse
Indicates that one entity uses or operates another entity that is located nearby or in close physical proximity.
-
D.
nearbyConnectionViaPassageways
Indicates that two locations are close to each other and are connected specifically through one or more passageways.
-
E.
hasNearbyDiscovery
Indicates that one entity has a discovery located in close physical or spatial proximity to it.
- 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_69e245467d9881908d6985bd0db7a1f1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1700edb608190ad786ff7fafea0da |
completed | April 29, 2026, 2:42 a.m. |
| PD | Predicate disambiguation | batch_69ee62855558819080da946c7b35a160 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 3:03 p.m.