Triple
T35748931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canteleu |
E1033262
|
entity |
| Predicate | departmentContext |
P183617
|
FINISHED |
| Object | Seine-Maritime |
—
|
NE NERFINISHED |
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: Seine-Maritime | Statement: [Canteleu, departmentContext, Seine-Maritime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentContext Context triple: [Canteleu, departmentContext, Seine-Maritime]
-
A.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
B.
organizationContext
Indicates that one entity operates within, is associated with, or is relevant to the organizational setting or environment defined by another entity.
-
C.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
-
D.
departmentReferenced
Indicates that one entity refers to or cites a specific department in some way.
-
E.
departmentNumber
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
- F. None of above. chosen
Provenance (4 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_69f76e119d508190a3873cb302063832 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a1f64f1081908cc2774840684310 |
completed | May 3, 2026, 7:28 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a162672481909773f8383d91159a |
completed | May 3, 2026, 7:26 p.m. |
Created at: May 3, 2026, 4:06 p.m.