Triple
T24728105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auto Pact between Canada and the United States |
E618209
|
entity |
| Predicate | effectOnCanada |
P157822
|
FINISHED |
| Object | stimulated growth of Canadian auto manufacturing |
—
|
LITERAL 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: stimulated growth of Canadian auto manufacturing | Statement: [Auto Pact between Canada and the United States, effectOnCanada, stimulated growth of Canadian auto manufacturing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnCanada Context triple: [Auto Pact between Canada and the United States, effectOnCanada, stimulated growth of Canadian auto manufacturing]
-
A.
effectOnUnitedStates
Indicates the impact, influence, or consequences that something has on the United States.
-
B.
effectOnRussia
Indicates the impact or consequences that something has on Russia.
-
C.
effectOnFrance
Indicates the impact, influence, or consequences that something has on France.
-
D.
impactOnTrade
Indicates a relationship where one entity causes or contributes to a change in the trade activities, volume, or conditions affecting another entity.
-
E.
resultEffectOnBrexit
Indicates the effect or impact that a particular result has on Brexit.
- F. None of above. chosen
Provenance (4 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_69e2fab772608190b74163751047ff50 |
completed | April 18, 2026, 3:29 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 18, 2026, 4:01 a.m.