Triple
T1066301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NHS Scotland |
E23217
|
entity |
| Predicate | numberOfTerritorialBoards |
P19531
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [NHS Scotland, numberOfTerritorialBoards, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTerritorialBoards Context triple: [NHS Scotland, numberOfTerritorialBoards, 14]
-
A.
numberOfTerritories
chosen
Indicates the total count of territories associated with a given entity.
-
B.
numberOfUnionTerritories
Indicates the total count of union territories associated with a given country or administrative entity.
-
C.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
D.
associatedConfederationCount
Indicates the number of distinct confederations with which an entity is associated.
-
E.
partitionedTerritory
Indicates that a larger territory has been divided into distinct parts or regions, typically with defined boundaries or administrative separation.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b9e1047481909af1cf8df2a01fff |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b736f1e881909bace735b38c0ade |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.