Triple
T27175594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Carolina ABLE savings program |
E683034
|
entity |
| Predicate | qualifiedExpenseCategory |
P24220
|
FINISHED |
| Object | education |
—
|
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: education | Statement: [North Carolina ABLE savings program, qualifiedExpenseCategory, education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: qualifiedExpenseCategory Context triple: [North Carolina ABLE savings program, qualifiedExpenseCategory, education]
-
A.
reimbursementCategory
Indicates the classification or type under which a reimbursement claim or expense is categorized.
-
B.
mainExpenditureCategory
Indicates the primary type or classification of spending to which a particular expenditure mainly belongs.
-
C.
payCategory
Indicates the classification of a payment or compensation into a specific category (such as type, purpose, or pay band) within a payment or payroll context.
-
D.
supportsSpendingCategory
chosen
Indicates that one entity allows, enables, or is compatible with making expenditures in a specified spending category.
-
E.
includesCreditCategory
Indicates that one entity contains or encompasses a specific credit-related category within its defined set or structure.
- 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_69eefad086808190ab89816c0c300476 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 27, 2026, 9:25 a.m.