Triple
T22880732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Welfare reform in Wisconsin |
E567458
|
entity |
| Predicate | effectOnIndicator |
P53074
|
FINISHED |
| Object | reduced welfare caseloads in Wisconsin |
—
|
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: reduced welfare caseloads in Wisconsin | Statement: [Welfare reform in Wisconsin, effectOnIndicator, reduced welfare caseloads in Wisconsin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnIndicator Context triple: [Welfare reform in Wisconsin, effectOnIndicator, reduced welfare caseloads in Wisconsin]
-
A.
effectOnOutput
Indicates how one factor, action, or condition influences or changes the resulting output of a process or system.
-
B.
effectOnRepresentation
Indicates how one entity influences, alters, or determines the form, quality, or characteristics of another entity’s representation.
-
C.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
D.
measuredEffect
Indicates that an action or process has produced a specific, quantified outcome or impact on something.
-
E.
eventEffect
chosen
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
- 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f5c1ed88190aeac131c5aff6102 |
completed | April 29, 2026, 3:47 a.m. |
| PD | Predicate disambiguation | batch_69ef3b6b2e2481908258156937b5a745 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:39 p.m.