Triple
T30355912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Small Business Research Initiative |
E772141
|
entity |
| Predicate | benefitToSMEs |
P66311
|
FINISHED |
| Object | funding for early-stage R&D |
—
|
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: funding for early-stage R&D | Statement: [Small Business Research Initiative, benefitToSMEs, funding for early-stage R&D]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitToSMEs Context triple: [Small Business Research Initiative, benefitToSMEs, funding for early-stage R&D]
-
A.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
B.
benefitsAre
chosen
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
C.
benefitsArea
Indicates that one entity provides advantages, improvements, or positive effects to a specified area or region.
-
D.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
E.
benefitsCategory
Indicates that one entity provides, falls under, or is associated with a particular category of benefits for another entity.
- 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_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 29, 2026, 7:57 p.m.