Triple
T16467393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valbonne |
E399970
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Sophia Antipolis |
E158509
|
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: Sophia Antipolis | Statement: [Valbonne, near, Sophia Antipolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophia Antipolis Context triple: [Valbonne, near, Sophia Antipolis]
-
A.
Sophia Antipolis
chosen
Sophia Antipolis is a major technology and research park in southeastern France, known as a European hub for telecommunications, information technology, and innovation.
-
B.
Juan-les-Pins
Juan-les-Pins is a seaside resort town on the French Riviera, known for its beaches, nightlife, and jazz festival.
-
C.
Bures-sur-Yvette
Bures-sur-Yvette is a suburban commune in the Île-de-France region of northern France, known for hosting part of the Paris-Saclay scientific and university cluster.
-
D.
Garches
Garches is a suburban commune in the western outskirts of Paris, France, known for its residential character and proximity to major Parisian business districts.
-
E.
Palaiseau
Palaiseau is a suburban commune in the southern outskirts of Paris, France, known for hosting major scientific and engineering institutions.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dcd707081908fb7ca91a8c09e0a |
completed | April 18, 2026, 7:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f5914dc81908c3b8cf999ee76a1 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:11 a.m.