Triple
T9299884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Quebec (1690) |
E223730
|
entity |
| Predicate | englishAim |
P87944
|
FINISHED |
| Object | to weaken French power in North America |
—
|
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: to weaken French power in North America | Statement: [Battle of Quebec (1690), englishAim, to weaken French power in North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: englishAim Context triple: [Battle of Quebec (1690), englishAim, to weaken French power in North America]
-
A.
englishFlagship
Indicates that an entity participates in or is associated with an English flagship program (typically an intensive, advanced English language or studies track).
-
B.
EnglishObjective
Indicates that an entity has English language proficiency or achievement as a goal or target to be attained.
-
C.
EnglishTactic
Indicates a tactical or strategic action carried out using the English language as the primary medium or method.
-
D.
EnglishStrength
Indicates the degree or level of proficiency or capability in using the English language.
-
E.
EnglishCommander
Indicates that one entity serves as a military commander or leader for English forces in relation to another entity.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08d070c881908bed41aada6f85ae |
completed | April 1, 2026, noon |
| PD | Predicate disambiguation | batch_69cc7a5ef1908190bc5ca166bb895af6 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:36 p.m.