Triple
T9069860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kleinburg |
E217336
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | city of Vaughan |
E40928
|
NE 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: city of Vaughan | Statement: [Kleinburg, partOf, city of Vaughan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Vaughan Context triple: [Kleinburg, partOf, city of Vaughan]
-
A.
Vaughan
Vaughan is a surname of Welsh origin that is notably associated with influential figures such as blues guitarist Stevie Ray Vaughan.
-
B.
Vaughan
chosen
Vaughan is a rapidly growing suburban city in the Greater Toronto Area known for its diverse communities, shopping and entertainment complexes, and attractions like Canada’s Wonderland.
-
C.
Township of Vaughan
The Township of Vaughan was a historic municipal subdivision in what is now the City of Vaughan, located north of Toronto in Ontario, Canada.
-
D.
Oakville, Ontario
Oakville, Ontario is a suburban town on Lake Ontario in the Greater Toronto Area, known for its affluent neighborhoods, harbors, and vibrant arts and cultural scene.
-
E.
Brampton
Brampton is a large suburban city in the Greater Toronto Area known for its diverse population and rapidly growing economy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83d5a7f48190b16c1e59bd43ede0 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc955d29548190a204efd3257b6d5d |
completed | April 1, 2026, 3:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0774f0d7c8190b071e5b161622355 |
completed | April 4, 2026, 2:28 a.m. |
Created at: March 30, 2026, 7:11 p.m.