Triple
T455656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirty Thirties |
E7226
|
entity |
| Predicate | affectedArea |
P1586
|
FINISHED |
| Object | Manitoba |
E15186
|
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: Manitoba | Statement: [Dirty Thirties, affectedArea, Manitoba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manitoba Context triple: [Dirty Thirties, affectedArea, Manitoba]
-
A.
Manitoba
chosen
Manitoba is a central Canadian province known for its vast prairies, numerous lakes, and northern boreal forests.
-
B.
Saskatchewan
Saskatchewan is a prairie and boreal province in western Canada known for its vast flat landscapes, agriculture, and significant natural resources.
-
C.
Alberta
Alberta is a western Canadian province known for its vast prairies, Rocky Mountains, and significant natural resource industries.
-
D.
Ontario
Ontario is Canada’s most populous province, home to the nation’s capital Ottawa and its largest city Toronto, and a major economic and cultural hub.
-
E.
Ontario
Ontario is a city in southwestern San Bernardino County, California, known as a major logistics and transportation hub anchored by Ontario International Airport and extensive freeway and rail connections.
- 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef9f772c8190863399c5ee1378fe |
completed | Feb. 28, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4c029f10c8190a755e230528f3b7b |
completed | March 1, 2026, 10:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.