Triple
T698689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caméra d’Or |
E13949
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Cannes |
E47528
|
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: Cannes | Statement: [Caméra d’Or, location, Cannes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cannes Context triple: [Caméra d’Or, location, 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.
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.
-
D.
Marseille
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
E.
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0dd4afc81909e4e869356006f33 |
completed | March 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a6787385d8819083f5b6336363743e |
completed | March 3, 2026, 5:58 a.m. |
Created at: March 1, 2026, 7:36 p.m.