Triple
T2571714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivia Chow |
E57677
|
entity |
| Predicate | electionContested |
P36644
|
FINISHED |
| Object | 2014 Toronto mayoral election |
—
|
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: 2014 Toronto mayoral election | Statement: [Olivia Chow, electionContested, 2014 Toronto mayoral election]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electionContested Context triple: [Olivia Chow, electionContested, 2014 Toronto mayoral election]
-
A.
hasOfficeContested
chosen
Indicates that an individual has been a candidate for a particular public office in an election.
-
B.
wasContestedIn
Indicates that an event, position, or decision was the subject of competition, dispute, or challenge within a particular context or proceeding.
-
C.
electoralCompetition
Indicates the degree to which multiple political candidates or parties actively contend against each other in an election for votes or office.
-
D.
alsoContestedIn
Indicates that the same issue, claim, or matter is being disputed or challenged in another context, case, or proceeding as well.
-
E.
contestedBy
Indicates that one party challenges, disputes, or opposes a claim, decision, or position held by another party.
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd383dce881909411a38c6d37bc3a |
completed | March 7, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69abd0ce4dcc8190b17a65abf9bd1bb0 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:48 p.m.