Triple
T2780832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States presidential election of 1832 |
E61687
|
entity |
| Predicate | electoralVotesReceivedByWilliamWirt |
P27351
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [United States presidential election of 1832, electoralVotesReceivedByWilliamWirt, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electoralVotesReceivedByWilliamWirt Context triple: [United States presidential election of 1832, electoralVotesReceivedByWilliamWirt, 7]
-
A.
electoralVotesReceived
chosen
Indicates that one entity received a specified number of electoral votes in an election from another entity or jurisdiction.
-
B.
electoralVotesWinner
Indicates that the subject is the candidate who received the highest number of electoral votes in a given election.
-
C.
WillkiePopularVotes
Indicates the number of popular votes received by Willkie in a given election or electoral context.
-
D.
numberOfElectors
Indicates the total count of electors associated with a given entity or context.
-
E.
electoralVoteOutcome
Indicates the result of an election in terms of how electoral votes are allocated or distributed among candidates or options.
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.