Triple
T10667468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rueil-Malmaison |
E251392
|
entity |
| Predicate | hasTypeOfUrbanArea |
P40854
|
FINISHED |
| Object | affluent residential suburb |
—
|
LITERAL 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: affluent residential suburb | Statement: [Rueil-Malmaison, hasTypeOfUrbanArea, affluent residential suburb]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfUrbanArea Context triple: [Rueil-Malmaison, hasTypeOfUrbanArea, affluent residential suburb]
-
A.
containsUrbanArea
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
B.
appliesToUrbanAreaType
Indicates that something (such as a rule, measure, or classification) is applicable specifically to a particular type or category of urban area.
-
C.
hasUrbanAreaApprox
Indicates an approximate measure or estimate of the size or extent of an entity’s urban area.
-
D.
hasUrbanClassification
chosen
Indicates that an entity is assigned a specific urban status or category within a defined classification system.
-
E.
hasUrbanDistrictFunction
Indicates that an entity serves the administrative or functional role of an urban district within a larger territorial or governance structure.
- F. None of above.
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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f3204bac8190b9bd8bfcc705b06b |
completed | April 9, 2026, 12:30 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8a93208190a573061387e2aebb |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:08 p.m.