Triple
T2593510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States presidential election, 1972 |
E58178
|
entity |
| Predicate | popularVoteLoserPercentage |
P6370
|
FINISHED |
| Object | 37.5% |
—
|
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: 37.5% | Statement: [United States presidential election, 1972, popularVoteLoserPercentage, 37.5%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularVoteLoserPercentage Context triple: [United States presidential election, 1972, popularVoteLoserPercentage, 37.5%]
-
A.
popularVotePercentageLoser
chosen
Indicates the percentage of the total popular vote received by the candidate or party that did not win the election.
-
B.
popularVoteLoser
Indicates that the subject became the winner of an election despite receiving fewer popular votes than at least one opponent.
-
C.
smithPopularVotePercentage
Indicates the percentage of the popular vote that was received by the entity named Smith in a given election or voting context.
-
D.
voterTurnoutPercentage
Indicates the proportion of eligible or registered voters who actually cast a ballot in a given election, expressed as a percentage.
-
E.
popularVoteMargin
Indicates the difference in the number or percentage of popular votes received by two candidates or options 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd426e2d4819081a07920b4d2a1cc |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d344988190a18dd93b13e002e6 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:49 p.m.