Triple
T13310640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D213 road |
E317053
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Saint-Brevin-les-Pins |
E330369
|
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: Saint-Brevin-les-Pins | Statement: [D213 road, passesNear, Saint-Brevin-les-Pins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Brevin-les-Pins Context triple: [D213 road, passesNear, Saint-Brevin-les-Pins]
-
A.
Saint-Brevin-les-Pins
chosen
Saint-Brevin-les-Pins is a seaside commune on France’s Atlantic coast, known for its sandy beaches, pine forests, and role as a gateway to the Loire estuary opposite Saint-Nazaire.
-
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.
Mandelieu-la-Napoule
Mandelieu-la-Napoule is a coastal resort town on the French Riviera in southeastern France, known for its beaches, marina, and proximity to Cannes.
-
D.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
E.
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.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f56abc8190951774a999e2ce11 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716e7b9a48190a33b04df8ad45ed8 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.