Triple
T30355910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Small Business Research Initiative |
E772141
|
entity |
| Predicate | benefitToPublicSector |
P32550
|
FINISHED |
| Object | access to novel technologies |
—
|
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: access to novel technologies | Statement: [Small Business Research Initiative, benefitToPublicSector, access to novel technologies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitToPublicSector Context triple: [Small Business Research Initiative, benefitToPublicSector, access to novel technologies]
-
A.
sectorBenefited
chosen
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
B.
retainedInPublicSectorAs
Indicates that an entity continues to be held, owned, or managed within the public sector in the specified role or capacity.
-
C.
areaOfPublicService
Indicates the specific public service domain or sector in which an entity operates, serves, or provides benefits.
-
D.
benefitsArea
Indicates that one entity provides advantages, improvements, or positive effects to a specified area or region.
-
E.
reformsBenefit
Indicates that certain reforms produce advantages, improvements, or positive outcomes for a particular entity or group.
- 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6df450014819099d118e5c2d697fa |
completed | May 3, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69f6de07836481908785cde9c511920b |
completed | May 3, 2026, 5:32 a.m. |
Created at: April 29, 2026, 7:57 p.m.