Triple
T15500644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1967 Chicago mayoral election |
E378942
|
entity |
| Predicate | popularVoteRunnerUpVotes |
P118893
|
FINISHED |
| Object | 287828 |
—
|
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: 287828 | Statement: [1967 Chicago mayoral election, popularVoteRunnerUpVotes, 287828]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularVoteRunnerUpVotes Context triple: [1967 Chicago mayoral election, popularVoteRunnerUpVotes, 287828]
-
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.
popularVoteOpponent
Indicates that one entity is the opponent of another in a popular vote or general election contest.
-
C.
popularVotes
Indicates the number of votes an entity (such as a candidate or option) receives directly from individual voters in an election or decision process.
-
D.
popularVoteCountOpponent
Indicates the number of votes received by the opposing candidate or party in a popular vote contest.
-
E.
popularVoteWinner
Indicates that the subject is the candidate who received the highest number of individual votes cast by the electorate in an election.
- 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_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. |
| PDg | Predicate description generation | batch_69ded57165288190979b7acb71ad5145 |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:54 a.m.