Triple
T10966787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1920 United States presidential election |
E259120
|
entity |
| Predicate | electoralVotesForLoser |
P9221
|
FINISHED |
| Object | 127 |
—
|
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: 127 | Statement: [1920 United States presidential election, electoralVotesForLoser, 127]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electoralVotesForLoser Context triple: [1920 United States presidential election, electoralVotesForLoser, 127]
-
A.
electoralVotesWinner
Indicates that the subject is the candidate who received the highest number of electoral votes in a given election.
-
B.
popularVoteLoserTotal
Indicates that the subject is the candidate who lost the election overall but received the specified total number of popular votes.
-
C.
opponentElectoralVotes
chosen
Indicates the number of electoral votes received by the opposing candidate or party in an election.
-
D.
popularVoteLoser
Indicates that the subject became the winner of an election despite receiving fewer popular votes than at least one opponent.
-
E.
electoralVotesReceived
Indicates that one entity received a specified number of electoral votes in an election from another entity or jurisdiction.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7714ab8588190874289f84a132fe1 |
completed | April 9, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_69d72e8c27cc81908050590b7a04cafd |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:24 p.m.