Triple
T34978687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1891 Chicago mayoral election |
E1008753
|
entity |
| Predicate | secondPlacePopularVotePercentage |
P160990
|
FINISHED |
| Object | 35.43 |
—
|
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: 35.43 | Statement: [1891 Chicago mayoral election, secondPlacePopularVotePercentage, 35.43]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondPlacePopularVotePercentage Context triple: [1891 Chicago mayoral election, secondPlacePopularVotePercentage, 35.43]
-
A.
secondPartyPopularVoteShare
Indicates the proportion of total popular votes received by the second party in an election.
-
B.
secondRoundRunnerUpVoteSharePercentage
Indicates the percentage of total votes received by the candidate who finished as runner-up in the second round of a contest or election.
-
C.
secondRoundVoteShareOf
Indicates the proportion of total votes an entity receives in the second round of a multi-round voting or election process.
-
D.
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.
-
E.
runnerUpVoteSharePercentage
chosen
Indicates the percentage of total votes received by the candidate or option that finished in second place.
- 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_69f76dc844a48190881951fffb83d17e |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff32b88bf48190b45afd1b60cb511c |
completed | May 9, 2026, 1:12 p.m. |
| PD | Predicate disambiguation | batch_69ff3031e18881908927b2ab452de863 |
completed | May 9, 2026, 1:01 p.m. |
Created at: May 3, 2026, 4:01 p.m.