Triple
T37992550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto—Danforth |
E947854
|
entity |
| Predicate | electorCountApproximate |
P145274
|
FINISHED |
| Object | 90000 |
—
|
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: 90000 | Statement: [Toronto—Danforth, electorCountApproximate, 90000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electorCountApproximate Context triple: [Toronto—Danforth, electorCountApproximate, 90000]
-
A.
approximateNumberOfVotersBefore
Indicates that one value represents an estimated count of voters that existed prior to a specified point in time or event.
-
B.
electorsApproximateRange
chosen
Indicates that one entity specifies an estimated or approximate numerical range for the number of electors associated with another entity.
-
C.
numberOfVoters
Indicates the total count of individuals who participated in a particular vote or election.
-
D.
hasElectorCount
Indicates that an entity is associated with a specific number of electors or electoral votes.
-
E.
electoralCountLocation
Indicates the place or jurisdiction in which an electoral vote count or tally is conducted or recorded.
- 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_69f76efa37088190be5416b7ef1ca275 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
Created at: May 3, 2026, 4:20 p.m.