Triple
T9883558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D559 road |
E180871
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Fréjus |
E214446
|
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: Fréjus | Statement: [D559 road, connects, Fréjus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fréjus Context triple: [D559 road, connects, Fréjus]
-
A.
Fréjus
chosen
Fréjus is a historic town and seaside resort on the French Riviera in southeastern France, known for its Roman ruins and Mediterranean coastline.
-
B.
Port de Saint-Raphaël
Port de Saint-Raphaël is a Mediterranean marina and harbor in the coastal town of Saint-Raphaël on the French Riviera, serving both leisure boating and local maritime activities.
-
C.
Hyères
Hyères is a coastal town in southeastern France known for its Mediterranean climate, historic old town, and nearby Golden Islands (Îles d’Hyères).
-
D.
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.
-
E.
Roquebrune-Cap-Martin
Roquebrune-Cap-Martin is a picturesque coastal commune on the French Riviera in southeastern France, known for its medieval village, Mediterranean views, and proximity to Monaco.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb453b388819095a5070399d9788d |
completed | April 2, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eaf0afcc81908d263df7651958e2 |
completed | April 5, 2026, 4:54 a.m. |
Created at: March 30, 2026, 8:38 p.m.