Triple
T3953969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Var |
E84932
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Le Lavandou |
E179579
|
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: Le Lavandou | Statement: [Var, contains, Le Lavandou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Lavandou Context triple: [Var, contains, Le Lavandou]
-
A.
Le Lavandou
chosen
Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
-
B.
Le Barcarès
Le Barcarès is a coastal commune in southern France on the Mediterranean Sea, known for its beaches, marina, and tourism.
-
C.
La Baule-Escoublac
La Baule-Escoublac is a renowned seaside resort town on France’s Atlantic coast, famous for its long sandy beach and upscale tourism.
-
D.
La Môle
La Môle is a central fictional nobleman and lover in Alexandre Dumas’s historical novel "Queen Margot," set amid the intrigues and violence of 16th-century France.
-
E.
Le Beausset
Le Beausset is a small commune in the Var department of southeastern France, near Toulon in the Provence-Alpes-Côte d'Azur region.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef93b8f5c8190bdb062a76b68b3e0 |
completed | March 9, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533ab58c08190ad83bf02571caaf2 |
completed | March 14, 2026, 10:08 a.m. |
Created at: March 9, 2026, 3:30 p.m.