Triple
T14910493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Senate election in Wisconsin |
E371246
|
entity |
| Predicate | turnoutAffectedBy |
P57252
|
FINISHED |
| Object | voter mobilization efforts |
—
|
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: voter mobilization efforts | Statement: [United States Senate election in Wisconsin, turnoutAffectedBy, voter mobilization efforts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turnoutAffectedBy Context triple: [United States Senate election in Wisconsin, turnoutAffectedBy, voter mobilization efforts]
-
A.
turnout
Indicates the number or proportion of participants who attend or take part in an event or activity.
-
B.
turnoutVariesByYear
Indicates that the level of turnout changes depending on the specific year considered.
-
C.
voterTurnoutChange
Indicates the amount or direction of change in voter turnout between two elections or time periods.
-
D.
standAffected
Indicates that an entity is in a state or position of being impacted or influenced by another entity or event.
-
E.
attendanceInfluence
chosen
Indicates how one entity’s presence or participation affects the attendance level or likelihood of attendance of another 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61c6b9c8190a92934d49b98fe46 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:26 a.m.