Triple
T101017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1932 United States presidential election |
E2039
|
entity |
| Predicate | popularVoteLoser |
P6369
|
FINISHED |
| Object | 15,761,254 |
—
|
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: 15,761,254 | Statement: [1932 United States presidential election, popularVoteLoser, 15,761,254]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularVoteLoser Context triple: [1932 United States presidential election, popularVoteLoser, 15,761,254]
-
A.
popularVoteWinner
Indicates that the subject is the candidate who received the highest number of individual votes cast by the electorate in an election.
-
B.
popularVoteRunnerUp
Indicates that one entity is the candidate who received the second-highest number of votes in a popular vote for the other entity’s election or contest.
-
C.
smithPopularVote
Indicates that Smith received a specified number or share of votes in a popular vote election or ballot.
-
D.
defeatedCandidate
Indicates that one candidate has won an election or contest against another candidate, causing the other to lose.
-
E.
electedCandidate
Indicates that a particular person has been chosen as the winner in an election for a given position or office.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69a2575d8a648190ad8e10d4b04e5e07 |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.