Triple
T14402257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | state budget of Finland |
E357099
|
entity |
| Predicate | expenditureArea |
P62936
|
FINISHED |
| Object | social security and welfare |
—
|
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: social security and welfare | Statement: [state budget of Finland, expenditureArea, social security and welfare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: expenditureArea Context triple: [state budget of Finland, expenditureArea, social security and welfare]
-
A.
expenditureFor
Indicates a relationship where a specific expenditure is made or allocated for a particular purpose, item, project, or entity.
-
B.
mainExpenditureCategory
chosen
Indicates the primary type or classification of spending to which a particular expenditure mainly belongs.
-
C.
expenditures
Indicates that an entity spends or allocates money or resources, typically specifying the amount, purpose, or category of that spending.
-
D.
typeOfSpendingAffected
Indicates that a particular kind or category of spending is influenced, changed, or impacted by another factor or event.
-
E.
incomeUsedFor
Indicates that some or all of an income amount is allocated or spent for a specified purpose, activity, or recipient.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de908500048190bb6a20fe318d5c62 |
completed | April 14, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.