Triple
T1573948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cavalaire-sur-Mer |
E33604
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Le Lavandou
Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
|
E179579
|
NE FINISHED |
How this triple was built (4 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: Le Lavandou | Statement: [Cavalaire-sur-Mer, near, Le Lavandou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Lavandou Context triple: [Cavalaire-sur-Mer, near, Le Lavandou]
-
A.
Leucate
Leucate is a coastal commune in southern France known for its Mediterranean beaches, wind sports, and scenic limestone cliffs.
-
B.
Mougins
Mougins is a picturesque hilltop village in southeastern France, renowned for its art scene, gastronomy, and association with many famous artists.
-
C.
Cimiez
Cimiez is a historic and upscale residential district in Nice, France, known for its Roman ruins, Belle Époque architecture, and cultural institutions.
-
D.
Saintes-Maries-de-la-Mer
Saintes-Maries-de-la-Mer is a coastal town in southern France known as a pilgrimage site and seaside resort at the edge of the Camargue wetlands.
-
E.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Le Lavandou Triple: [Cavalaire-sur-Mer, near, Le Lavandou]
Generated description
Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Le Lavandou Target entity description: Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
-
A.
Leucate
Leucate is a coastal commune in southern France known for its Mediterranean beaches, wind sports, and scenic limestone cliffs.
-
B.
Mougins
Mougins is a picturesque hilltop village in southeastern France, renowned for its art scene, gastronomy, and association with many famous artists.
-
C.
Cimiez
Cimiez is a historic and upscale residential district in Nice, France, known for its Roman ruins, Belle Époque architecture, and cultural institutions.
-
D.
Saintes-Maries-de-la-Mer
Saintes-Maries-de-la-Mer is a coastal town in southern France known as a pilgrimage site and seaside resort at the edge of the Camargue wetlands.
-
E.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
- F. None of above. chosen
Provenance (5 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908bcd87881908b911314a30dd327 |
completed | March 5, 2026, 4:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad4028bc5881909dbe847229dd63bb |
completed | March 8, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_69ad41930d208190b34531e3f35fa58b |
completed | March 8, 2026, 9:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad422752348190b42ebc3781a6e8a5 |
completed | March 8, 2026, 9:32 a.m. |
Created at: March 4, 2026, 7:27 p.m.