Triple
T28330812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seine-Saint-Denis |
E717532
|
entity |
| Predicate | isSuburbanDepartmentOf |
P168102
|
FINISHED |
| Object | Paris metropolitan area |
—
|
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: Paris metropolitan area | Statement: [Seine-Saint-Denis, isSuburbanDepartmentOf, Paris metropolitan area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSuburbanDepartmentOf Context triple: [Seine-Saint-Denis, isSuburbanDepartmentOf, Paris metropolitan area]
-
A.
isSuburbanDistrict
Indicates that a district is located in and characterized as part of the suburbs of a larger urban area.
-
B.
isSuburbanMunicipality
Indicates that a municipality is located in a suburban area, typically surrounding or adjacent to a larger urban center.
-
C.
isSuburbanArea
Indicates that a location is characterized as a suburban area, typically lying between urban and rural regions and exhibiting suburban development patterns.
-
D.
isSuburbanHub
Indicates that a location functions as a primary activity or transit center within a suburban area, serving surrounding neighborhoods.
-
E.
isUrbanDepartment
Indicates that a department operates within, serves, or is designated for an urban (city) area.
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f673633d288190b52ceb9f8a057c44 |
completed | May 2, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 28, 2026, 12:32 a.m.