Triple
T18918122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Route Napoléon |
E462773
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Grasse |
—
|
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: Grasse | Statement: [Route Napoléon, passesThrough, Grasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grasse Context triple: [Route Napoléon, passesThrough, Grasse]
-
A.
Grasse
chosen
Grasse is a town in southeastern France renowned as the world’s perfume capital and a historic center of the fragrance industry.
-
B.
Aix-en-Provence
Aix-en-Provence is a historic and picturesque city in southern France, renowned for its Provençal charm, fountains, and as the hometown of painter Paul Cézanne.
-
C.
Saint-Raphaël
Saint-Raphaël is a commune in northern Haiti known for its agricultural activities and historical significance dating back to the colonial era.
-
D.
Saint-Raphaël
Saint-Raphaël is a coastal resort town on the French Riviera known for its Mediterranean beaches, yacht marina, and scenic Esterel Massif backdrop.
-
E.
Villeneuve-Loubet
Villeneuve-Loubet is a coastal commune in southeastern France known for its beaches, marina, and proximity to Nice on the French Riviera.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c62884988190a362ada1a0a47134 |
completed | April 20, 2026, 6:22 a.m. |
Created at: April 10, 2026, 11:59 a.m.