Triple
T15500645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1967 Chicago mayoral election |
E378942
|
entity |
| Predicate | popularVoteRunnerUpPercentage |
P6370
|
FINISHED |
| Object | 26.98 |
—
|
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: 26.98 | Statement: [1967 Chicago mayoral election, popularVoteRunnerUpPercentage, 26.98]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularVoteRunnerUpPercentage Context triple: [1967 Chicago mayoral election, popularVoteRunnerUpPercentage, 26.98]
-
A.
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.
-
B.
rulingPartyPopularVotePercentage
Indicates the percentage of the total popular vote received by the party currently in power or holding the ruling position.
-
C.
popularVotePercentageLoser
chosen
Indicates the percentage of the total popular vote received by the candidate or party that did not win the election.
-
D.
popularVoteLoserTotal
Indicates that the subject is the candidate who lost the election overall but received the specified total number of popular votes.
-
E.
popularVoteShare
Indicates the proportion of all votes cast in an election that were received by a particular candidate, party, or option.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69ded2896a9c8190a8b9627deb3c17b4 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:54 a.m.