Triple
T6770356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1872 United States presidential election |
E155026
|
entity |
| Predicate | electoralVoteCountOpponent |
P9221
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [1872 United States presidential election, electoralVoteCountOpponent, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electoralVoteCountOpponent Context triple: [1872 United States presidential election, electoralVoteCountOpponent, 0]
-
A.
opponentElectoralVotes
chosen
Indicates the number of electoral votes received by the opposing candidate or party in an election.
-
B.
electoralVoteRunnerUp
Indicates that the subject is the candidate who received the second-highest number of electoral votes in a given election.
-
C.
electoralVoteFor
Indicates that a specified number of electoral votes are allocated or cast in favor of a particular candidate or option in an election.
-
D.
voteShareMainOpponent
Indicates the proportion of total votes received by the primary opposing candidate or party in an election.
-
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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2347fb48190a44c03317b5ecfd7 |
completed | March 27, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69c6d094105881909c5806eb4afa6306 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:13 p.m.