Triple
T174895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ontario |
E3554
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | London, Ontario |
E24077
|
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: London, Ontario | Statement: [Ontario, containsCity, London, Ontario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: London, Ontario Context triple: [Ontario, containsCity, London, Ontario]
-
A.
London, Ontario
chosen
London, Ontario is a mid-sized Canadian city in southwestern Ontario known for its educational institutions, healthcare sector, and role as a regional economic and cultural hub.
-
B.
Windsor, Ontario
Windsor, Ontario is a Canadian city in southwestern Ontario known as a major automotive and manufacturing hub situated directly across the river from Detroit, Michigan.
-
C.
Brampton
Brampton is a large suburban city in the Greater Toronto Area known for its diverse population and rapidly growing economy.
-
D.
Toronto
Toronto is the largest city in Canada and a major cultural, financial, and media hub located in the province of Ontario.
-
E.
Alliston, Ontario, Canada
Alliston, Ontario, Canada is a small community best known as the birthplace of Sir Frederick Banting, co-discoverer of insulin.
- 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a258e32da88190ad9485aecd0bf08f |
completed | Feb. 28, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36cecd7548190afb12addafc7bbf5 |
completed | Feb. 28, 2026, 10:32 p.m. |
Created at: Feb. 28, 2026, 2:39 a.m.