Triple
T101020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1932 United States presidential election |
E2039
|
entity |
| Predicate | voterTurnout |
P1234
|
FINISHED |
| Object | 52.6% |
—
|
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: 52.6% | Statement: [1932 United States presidential election, voterTurnout, 52.6%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: voterTurnout Context triple: [1932 United States presidential election, voterTurnout, 52.6%]
-
A.
voterTurnoutPercentage
chosen
Indicates the proportion of eligible or registered voters who actually cast a ballot in a given election, expressed as a percentage.
-
B.
voterTurnoutChange
Indicates the amount or direction of change in voter turnout between two elections or time periods.
-
C.
electoralBase
Indicates the group of voters or supporters that primarily backs or sustains a particular candidate, party, or political movement in elections.
-
D.
electoralStatus
Indicates the current state or condition of an entity in relation to an election process (e.g., running, elected, defeated, or not a candidate).
-
E.
popularVoteWinner
Indicates that the subject is the candidate who received the highest number of individual votes cast by the electorate in an election.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2563921f8819087f720b1c803579f |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.