Triple
T17289172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roussillon plain |
E419737
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Le Barcarès |
E392501
|
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 Barcarès | Statement: [Roussillon plain, hasTown, Le Barcarès]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Barcarès Context triple: [Roussillon plain, hasTown, Le Barcarès]
-
A.
Le Barcarès
chosen
Le Barcarès is a coastal commune in southern France on the Mediterranean Sea, known for its beaches, marina, and tourism.
-
B.
Le Gâvre
Le Gâvre is a small commune in the Loire-Atlantique department in western France, known for its extensive forested area.
-
C.
Pas de Peyrol
Pas de Peyrol is a high mountain pass in France’s Massif Central, known as the highest road pass in the Cantal department and a key access point to the Puy Mary.
-
D.
La Bessiere
La Bessiere is a key character in the 1930 romantic drama film "Morocco," involved in the central love triangle alongside the leads.
-
E.
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.
- 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_69d886db32608190a61e18862c5a8af6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e43781b7808190a0528ee9c54a0c66 |
completed | April 19, 2026, 2:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a017957c738819087341bcd51b55114 |
completed | May 11, 2026, 6:38 a.m. |
Created at: April 10, 2026, 5:40 a.m.