Triple
T16173532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Barcarès |
E392501
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Leucate |
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 | Statement: [Le Barcarès, near, Leucate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leucate Context triple: [Le Barcarès, near, Leucate]
-
A.
Leucate
chosen
Leucate is a coastal commune in southern France known for its Mediterranean beaches, wind sports, and scenic limestone cliffs.
-
B.
Camprodon
Camprodon is a small town in the Catalan Pyrenees of northeastern Spain, known for its scenic mountain setting and historic Romanesque architecture.
-
C.
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.
-
D.
Frontignan
Frontignan is a coastal commune in southern France known for its Muscat wine production and Mediterranean setting near Sète.
-
E.
Sainte-Maxime
Sainte-Maxime is a seaside resort town on the French Riviera known for its Mediterranean beaches, marina, and views across the bay to Saint-Tropez.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb9b8208190b60874cec7a3a98e |
completed | April 17, 2026, 11:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000787d3fc8190a32d53a177fedb6d |
completed | May 10, 2026, 4:20 a.m. |
Created at: April 10, 2026, 5:02 a.m.