Triple
T4627834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toulon–Hyères Airport |
E101141
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hyères |
E478196
|
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: Hyères | Statement: [Toulon–Hyères Airport, locatedIn, Hyères]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hyères Context triple: [Toulon–Hyères Airport, locatedIn, Hyères]
-
A.
Hyères
chosen
Hyères is a coastal town in southeastern France known for its Mediterranean climate, historic old town, and nearby Golden Islands (Îles d’Hyères).
-
B.
Gardanne
Gardanne is a commune in southern France known for its industrial heritage and location between Marseille and Aix-en-Provence.
-
C.
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.
-
D.
Villefranche-sur-Mer
Villefranche-sur-Mer is a picturesque coastal town in southeastern France known for its deep natural harbor, colorful old town, and scenic setting on the Mediterranean Sea.
-
E.
Brignoles
Brignoles is a historic town in southeastern France’s Var department, known for its medieval center and former role as a residence of the Counts of Provence.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a2e9780819081add547c760abc9 |
completed | March 20, 2026, 2:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becf98d9cc8190a06d87027dec5680 |
completed | March 21, 2026, 5:04 p.m. |
Created at: March 20, 2026, 1:13 p.m.