Triple
T15180901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Martin-Vésubie |
E362739
|
entity |
| Predicate | distanceFromNice |
P117026
|
FINISHED |
| Object | about 60 kilometres north of Nice |
—
|
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: about 60 kilometres north of Nice | Statement: [Saint-Martin-Vésubie, distanceFromNice, about 60 kilometres north of Nice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromNice Context triple: [Saint-Martin-Vésubie, distanceFromNice, about 60 kilometres north of Nice]
-
A.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
B.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
C.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
D.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
E.
distanceFromAvignon
Indicates the spatial distance separating a given entity or location from Avignon.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00664caac81909bee1268264769f8 |
completed | April 15, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69deb97bd8bc8190b2ad4888f97cf963 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:09 a.m.