Triple
T3293391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Nighbor |
E69152
|
entity |
| Predicate | team |
P3756
|
FINISHED |
| Object | Toronto Ontarios |
E1525
|
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: Toronto Ontarios | Statement: [Frank Nighbor, team, Toronto Ontarios]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toronto Ontarios Context triple: [Frank Nighbor, team, Toronto Ontarios]
-
A.
Toronto
chosen
Toronto is the largest city in Canada and a major cultural, financial, and media hub located in the province of Ontario.
-
B.
London, Ontario
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.
-
C.
Aurora, Ontario
Aurora, Ontario is a suburban town in the Greater Toronto Area known for its residential communities, historic downtown, and role as a regional commercial and commuter hub.
-
D.
Wellington, Ontario
Wellington, Ontario is a small lakeside community in Prince Edward County known for its wineries, beaches, and vibrant arts and culinary scene.
-
E.
Mississauga, Ontario, Canada
Mississauga, Ontario, Canada is a large suburban city west of Toronto known for its diverse population, major corporate headquarters, and proximity to Toronto Pearson International Airport.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb074f35081909dd3c8a09544b5f1 |
completed | March 8, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367e9e414819088ec4b05495b86e1 |
completed | March 13, 2026, 1:27 a.m. |
Created at: March 8, 2026, 3:10 p.m.