Triple
T2529136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vallauris |
E56112
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Antibes |
E65894
|
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: Antibes | Statement: [Vallauris, locatedNear, Antibes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antibes Context triple: [Vallauris, locatedNear, Antibes]
-
A.
Antibes
chosen
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.
-
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.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
D.
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.
-
E.
Juan-les-Pins
Juan-les-Pins is a seaside resort town on the French Riviera, known for its beaches, nightlife, and jazz festival.
- 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd25903f08190b46e12d32278daca |
completed | March 7, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98a8887c8190bd00eaf48bc77781 |
completed | March 10, 2026, 4:06 a.m. |
Created at: March 6, 2026, 9:46 p.m.