Triple
T884614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater London |
E19101
|
entity |
| Predicate | hasNumberOfDistricts |
P1679
|
FINISHED |
| Object | 33 local authority districts including City of London |
—
|
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: 33 local authority districts including City of London | Statement: [Greater London, hasNumberOfDistricts, 33 local authority districts including City of London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfDistricts Context triple: [Greater London, hasNumberOfDistricts, 33 local authority districts including City of London]
-
A.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
B.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
C.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
D.
containsDistrictMunicipality
Indicates that an administrative region includes one or more district municipalities within its boundaries.
-
E.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
- 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ae787bf081909533082ca013624a |
completed | March 1, 2026, 9:24 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8ff8c48190a33b00acf65c1276 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.