Triple
T22435059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bionnassay–Mont Blanc traverse |
E554598
|
entity |
| Predicate | hasTypicalEndPoint |
P390
|
FINISHED |
| Object | Gouter route descent |
—
|
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: Gouter route descent | Statement: [Bionnassay–Mont Blanc traverse, hasTypicalEndPoint, Gouter route descent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalEndPoint Context triple: [Bionnassay–Mont Blanc traverse, hasTypicalEndPoint, Gouter route descent]
-
A.
hasEndpointType
Indicates that an entity has a specific type or classification assigned to one of its endpoints.
-
B.
hasEndpointStation
Indicates that something (such as a route, line, or service) has a specific station as one of its terminal endpoints.
-
C.
endPoint
chosen
Indicates the terminal location, limit, or final state reached by an object, process, or path in a given relationship or action.
-
D.
hasEndpointCity
Indicates that a route, connection, or path terminates at a particular city as one of its endpoints.
-
E.
hasDirectionEndpoints
Indicates that a directional element is defined or bounded by specific start and end points.
- 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15adda0e48190825a5b705ae52d5b |
completed | April 29, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69e898a327948190beee5e168006a0a7 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:47 p.m.