Triple
T9706938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carla-le-Comte |
E234922
|
entity |
| Predicate | hasDepartment |
P35
|
FINISHED |
| Object | Ariège |
E109578
|
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: Ariège | Statement: [Carla-le-Comte, hasDepartment, Ariège]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ariège Context triple: [Carla-le-Comte, hasDepartment, Ariège]
-
A.
Ariège
Ariège is a river in southwestern France that flows through the Pyrenees before joining the Garonne.
-
B.
Ariège
chosen
Ariège is a department in southwestern France, known for its Pyrenean landscapes, medieval castles, and rich Occitan culture.
-
C.
Ardèche
Ardèche is a department in southeastern France known for its dramatic river gorges, limestone caves, and scenic rural landscapes.
-
D.
Vallée de l’Agly
Vallée de l’Agly is a wine-producing area in Roussillon, southern France, known for its rugged terrain and diverse terroirs that yield distinctive red, white, and fortified wines.
-
E.
Ariège River
The Ariège River is a river in southwestern France that flows through the Pyrenees and the town of Foix before joining the Garonne.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9da4c53c81908ba4bfe4d9ca8814 |
completed | April 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c40329448190bccac60a20dcb9d1 |
completed | April 5, 2026, 2:08 a.m. |
Created at: March 30, 2026, 8:19 p.m.