Triple
T10659229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Paul-de-Vence |
E251178
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Provence |
E269383
|
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: Provence | Statement: [Saint-Paul-de-Vence, region, Provence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Provence Context triple: [Saint-Paul-de-Vence, region, Provence]
-
A.
Provence
chosen
Provence is a historic region in southeastern France known for its picturesque lavender fields, Mediterranean coastline, and rich cultural and culinary traditions.
-
B.
Languedoc
Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
-
C.
Provence-Alpes-Côte d’Azur
Provence-Alpes-Côte d’Azur is a region in southeastern France known for its Mediterranean coastline, picturesque villages, and cultural hubs such as Marseille and Nice.
-
D.
Southern France
Southern France is a culturally rich and geographically diverse region known for its Mediterranean coastline, historic cities, and renowned cuisine and wine.
-
E.
Occitanie
Occitanie is a large administrative region in southern France known for its Mediterranean coastline, historic cities like Toulouse and Montpellier, and diverse landscapes ranging from coastal plains to the Pyrenees.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6e0174dc4819093e577993c65ed32 |
completed | April 8, 2026, 11:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d98857ec248190bb655d36981a000a |
completed | April 10, 2026, 11:31 p.m. |
Created at: April 8, 2026, 9:07 p.m.