Triple
T10136316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arrondissement of Saint-Omer |
E226863
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Arques |
E353340
|
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: Arques | Statement: [Arrondissement of Saint-Omer, contains, Arques]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arques Context triple: [Arrondissement of Saint-Omer, contains, Arques]
-
A.
Arques
chosen
Arques is a river in northern France that flows through the Normandy region and reaches the English Channel at the port city of Dieppe.
-
B.
Montlhéry
Montlhéry is a commune in northern France best known for its historic motor racing circuit, the Autodrome de Linas-Montlhéry.
-
C.
Courcier
Courcier was a French publishing house known for issuing important mathematical and scientific works in the early 19th century.
-
D.
Desnos
Desnos is the surname of Robert Desnos, a notable French surrealist poet and member of the Resistance during World War II.
-
E.
Larroquette
Larroquette is the surname of John Larroquette, an American actor best known for his Emmy-winning role as Dan Fielding on the sitcom "Night Court."
- 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_69ca8433ec308190b8b25a6fe359c34c |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cde87fae288190bb4f13e1ae90f50a |
completed | April 2, 2026, 3:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5e4664081908c1821006bd2f57f |
completed | April 5, 2026, 10:44 p.m. |
Created at: March 30, 2026, 9:06 p.m.