Triple
T22790456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2019 Chicago mayoral election |
E564094
|
entity |
| Predicate | numberOfFirstRoundCandidates |
P53820
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [2019 Chicago mayoral election, numberOfFirstRoundCandidates, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFirstRoundCandidates Context triple: [2019 Chicago mayoral election, numberOfFirstRoundCandidates, 14]
-
A.
numberOfCandidates
chosen
Indicates the total count of candidates associated with a given entity or context.
-
B.
firstRoundLeadingCandidate
Indicates that the subject is the candidate who is leading in vote count after the first round of an election or selection process.
-
C.
numberOfMajorCandidates
Indicates the count of major candidates associated with a given entity or event.
-
D.
firstRound
Indicates that the referenced event, match, or interaction occurs in the initial or opening round of a multi-round process or competition.
-
E.
secondRoundCandidate
Indicates that an entity has advanced past an initial selection stage and is being considered as a candidate in a second round of evaluation or decision-making.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c3545fc819084af67cc25e94839 |
completed | April 29, 2026, 3:34 a.m. |
| PD | Predicate disambiguation | batch_69eed2c32e8c8190b73bb9965ed47d64 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:29 p.m.