Triple
T25237567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Windrush generation |
E632379
|
entity |
| Predicate | typicalEmploymentSector |
P143750
|
FINISHED |
| Object | National Health Service |
—
|
NE NERFINISHED |
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: National Health Service | Statement: [Windrush generation, typicalEmploymentSector, National Health Service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEmploymentSector Context triple: [Windrush generation, typicalEmploymentSector, National Health Service]
-
A.
typicalConstituentSector
Indicates that something is a usual or characteristic sector that forms part of a larger whole or system.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
economicSectors
chosen
Indicates a relationship that associates entities with the economic sectors or industries in which they operate or to which they belong.
-
D.
typicalEmployer
Indicates that one entity is the kind of organization or person that commonly or usually employs the other entity.
-
E.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
- 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_69e75a8ec5f88190b9eba06ae42b413a |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69fbbc49da8c8190902bbb05d2477cab |
completed | May 6, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69fbb13f34b08190bbbb220ac1e6e666 |
completed | May 6, 2026, 9:23 p.m. |
Created at: April 21, 2026, 1:07 p.m.