Triple
T23346575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cagnes-sur-Mer station |
E591881
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Cannes |
—
|
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: Cannes | Statement: [Cagnes-sur-Mer station, connectsTo, Cannes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cannes Context triple: [Cagnes-sur-Mer station, connectsTo, Cannes]
-
A.
Cannes
chosen
Cannes is a glamorous resort city on the French Riviera, internationally renowned for its luxury tourism, beaches, and role as a global center of the film industry.
-
B.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
C.
Sophia Antipolis
Sophia Antipolis is a major technology and research park in southeastern France, known as a European hub for telecommunications, information technology, and innovation.
-
D.
Grasse
Grasse is a town in southeastern France renowned as the world’s perfume capital and a historic center of the fragrance industry.
-
E.
Antibes
Antibes is a historic resort town on the French Riviera known for its Mediterranean coastline, old town, and association with artists such as Pablo Picasso.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19837874481908f1a530261a34819 |
completed | April 29, 2026, 5:33 a.m. |
Created at: April 17, 2026, 5:19 p.m.