Triple
T842916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Toronto Area |
E18214
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mississauga |
E30860
|
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: Mississauga | Statement: [Greater Toronto Area, contains, Mississauga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mississauga Context triple: [Greater Toronto Area, contains, Mississauga]
-
A.
Mississauga
chosen
Mississauga is a large, diverse Canadian city in the Greater Toronto Area known for its major airport, corporate headquarters, and extensive suburban communities.
-
B.
Welland
Welland is a city in the Niagara Region of southern Ontario, Canada, known for the Welland Canal that connects Lake Ontario and Lake Erie.
-
C.
Orillia
Orillia is a small city in central Ontario, Canada, known for its lakeside setting on Lake Couchiching and Lake Simcoe and its popular waterfront and cultural festivals.
-
D.
Oshawa
Oshawa is a city in southern Ontario, Canada, known historically as a major automotive manufacturing center and part of the Greater Toronto Area.
-
E.
Etobicoke
Etobicoke is a large suburban district in the western part of Toronto, Ontario, known for its residential neighborhoods, parks, and industrial areas along the waterfront.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abe8a0bc81909b54af465e67be1f |
completed | March 1, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac537700548190aa8ead8c966eb329 |
completed | March 7, 2026, 4:33 p.m. |
Created at: March 1, 2026, 7:38 p.m.