Triple
T7183681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leucate |
E167515
|
entity |
| Predicate | hasPortion |
P35
|
FINISHED |
| Object | Leucate-Plage |
E167515
|
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: Leucate-Plage | Statement: [Leucate, hasPortion, Leucate-Plage]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leucate-Plage Context triple: [Leucate, hasPortion, Leucate-Plage]
-
A.
Leucate
chosen
Leucate is a coastal commune in southern France known for its Mediterranean beaches, wind sports, and scenic limestone cliffs.
-
B.
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.
-
C.
Valras-Plage
Valras-Plage is a seaside resort town on France’s Mediterranean coast, known for its sandy beaches and tourism.
-
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.
Lamalou-les-Bains
Lamalou-les-Bains is a spa town in southern France known for its thermal baths and therapeutic treatments.
- 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_69c6888a7c548190a3d39b52a393080f |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8de114c8190ad8fde0654526482 |
completed | March 27, 2026, 8:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b94b058481909da9d5b21a5201de |
completed | March 28, 2026, 11:19 a.m. |
Created at: March 27, 2026, 2:49 p.m.