Triple
T28574409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto–Sudbury |
E723195
|
entity |
| Predicate | hasProvinceCapitalOnRoute |
P65699
|
FINISHED |
| Object | Toronto |
—
|
NE NERFINISHED |
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: Toronto | Statement: [Toronto–Sudbury, hasProvinceCapitalOnRoute, Toronto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProvinceCapitalOnRoute Context triple: [Toronto–Sudbury, hasProvinceCapitalOnRoute, Toronto]
-
A.
isInProvinceWithCapital
Indicates that a place is located within a province whose administrative capital is a specified city.
-
B.
servesProvinceCapital
Indicates that a city functions as the administrative or service center for a given province’s capital.
-
C.
hasMajorCityOnRoute
chosen
Indicates that a major city lies along, or is directly served by, a specified route or path between locations.
-
D.
hasCountryCapitalOfProvince
Indicates that a country has a specific city that serves as the capital of one of its provinces.
-
E.
associatedWithProvinceCapital
Indicates that an entity has a relationship or connection to the capital city of a specific province.
- 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_69f01d7e97708190ae9e77ee66a68abd |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fef3ceef648190b58027c93d757438 |
completed | May 9, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69fef359da2c819091a034387b08821f |
completed | May 9, 2026, 8:42 a.m. |
Created at: April 28, 2026, 4:11 a.m.