Triple
T5120799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aéroports de la Côte d’Azur |
E115457
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Saint-Tropez |
E64451
|
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: Saint-Tropez | Statement: [Aéroports de la Côte d’Azur, servesCity, Saint-Tropez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Tropez Context triple: [Aéroports de la Côte d’Azur, servesCity, Saint-Tropez]
-
A.
Saint-Tropez
chosen
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
B.
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.
-
C.
Cagnes-sur-Mer
Cagnes-sur-Mer is a coastal town on the French Riviera in southeastern France, known for its Mediterranean beaches and historic hilltop village.
-
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.
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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78015ad88190a3e51da494c19e30 |
completed | March 20, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bef7edd6548190acbdc74eb1d0028d |
completed | March 21, 2026, 7:56 p.m. |
Created at: March 20, 2026, 1:42 p.m.