Triple
T1459203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Civic Railways |
E31470
|
entity |
| Predicate | serviceArea |
P82
|
FINISHED |
| Object | East Toronto |
E36384
|
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: East Toronto | Statement: [Toronto Civic Railways, serviceArea, East Toronto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: East Toronto Context triple: [Toronto Civic Railways, serviceArea, East Toronto]
-
A.
Eastern Toronto
chosen
Eastern Toronto is the part of Toronto that includes areas such as Scarborough and other eastern neighborhoods of the city.
-
B.
Midtown Toronto
Midtown Toronto is a central district of Toronto known for its mix of residential neighborhoods, historic landmarks, and vibrant commercial areas.
-
C.
North York
North York is a major district in the north end of Toronto, Ontario, known for its dense urban development, shopping centers, and mixed residential and commercial areas.
-
D.
Downtown Toronto
Downtown Toronto is the city’s primary central business district and cultural core, known for its dense skyline, major attractions, and vibrant urban life.
-
E.
Vaughan
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.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c59c1c288190be08064f2d351b2b |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad15a299ac81908f37085a107c8e9f |
completed | March 8, 2026, 6:22 a.m. |
Created at: March 1, 2026, 8 p.m.