Triple
T2637571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penge Urban District |
E59784
|
entity |
| Predicate | administeredAreaType |
P6822
|
FINISHED |
| Object | suburban area |
—
|
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: suburban area | Statement: [Penge Urban District, administeredAreaType, suburban area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administeredAreaType Context triple: [Penge Urban District, administeredAreaType, suburban area]
-
A.
typeOfAdministration
Indicates the specific form or system of administration applied to or governing an entity.
-
B.
hasAdministrativeArea
Indicates that one entity serves as the governing or jurisdictional area responsible for administering another entity.
-
C.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
D.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
E.
usesAdministrativeDivisionType
Indicates that one entity employs or applies a particular type of administrative division as part of its territorial or organizational 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8e3190081908ea828fe79569cc9 |
completed | March 7, 2026, 7:50 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.