Triple
T55613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshua Bengio |
E1098
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Montreal |
E2604
|
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: Montreal | Statement: [Yoshua Bengio, residence, Montreal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montreal Context triple: [Yoshua Bengio, residence, Montreal]
-
A.
Montreal
chosen
Montreal is the largest city in Quebec, Canada, known for its vibrant bilingual culture, historic architecture, and status as a major economic and cultural center.
-
B.
Quebec City
Quebec City is the historic capital of the Canadian province of Quebec, renowned for its well-preserved fortified old town and rich French colonial heritage.
-
C.
Ottawa
Ottawa is the capital city of Canada, located in eastern Ontario along the Ottawa River and known for its federal government institutions, cultural landmarks, and bilingual character.
-
D.
Toronto
Toronto is the largest city in Canada and a major cultural, financial, and media hub located in the province of Ontario.
-
E.
Vancouver
Vancouver is a major coastal city in western Canada known for its scenic natural surroundings, multicultural population, and role as a hub for film, technology, and tourism.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b07f4a881909e32115e84da02a3 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a29e43eaf88190b153139b9710d5d2 |
completed | Feb. 28, 2026, 7:50 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.