Triple
T27381074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toshima |
E691228
|
entity |
| Predicate | hasDenseUrbanResidentialAreas |
P147867
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Toshima, hasDenseUrbanResidentialAreas, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDenseUrbanResidentialAreas Context triple: [Toshima, hasDenseUrbanResidentialAreas, true]
-
A.
hasHousingDensity
Indicates the relationship between an area and the concentration of housing units within that area, typically measured as units per unit of land.
-
B.
hasUrbanDistrictCount
Indicates the number of urban districts associated with a given entity.
-
C.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
D.
hasUrbanSectionsIn
chosen
Indicates that an entity includes or contains sections that are classified as urban within a specified area or region.
-
E.
hasUrbanUnits
Indicates that an entity possesses or includes one or more urban units (such as cities, towns, or urbanized areas) within its scope or 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_69ef52022538819081f873d0c84a6dd6 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: April 27, 2026, 12:22 p.m.