Triple
T15181441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loup |
E362753
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Cagnes-sur-Mer |
E393279
|
NE 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: Cagnes-sur-Mer | Statement: [Loup, flowsThrough, Cagnes-sur-Mer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cagnes-sur-Mer Context triple: [Loup, flowsThrough, Cagnes-sur-Mer]
-
A.
Cagnes-sur-Mer
chosen
Cagnes-sur-Mer is a coastal town on the French Riviera in southeastern France, known for its Mediterranean beaches and historic hilltop village.
-
B.
Le Cannet
Le Cannet is a commune in the Alpes-Maritimes department of southeastern France, located just north of Cannes on the French Riviera.
-
C.
Villefranche-sur-Mer
Villefranche-sur-Mer is a picturesque coastal town in southeastern France known for its deep natural harbor, colorful old town, and scenic setting on the Mediterranean Sea.
-
D.
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.
-
E.
La Seyne-sur-Mer
La Seyne-sur-Mer is a coastal town in southeastern France on the Mediterranean, historically known for its major shipbuilding industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006663ad48190986b680001be0e9b |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c2d21348190a8045cf1847396e0 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 3:09 a.m.