Triple
T5855075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | statue of William Lyon Mackenzie King |
E130130
|
entity |
| Predicate | subjectIs |
P22381
|
FINISHED |
| Object | longest-serving prime minister of Canada |
—
|
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: longest-serving prime minister of Canada | Statement: [statue of William Lyon Mackenzie King, subjectIs, longest-serving prime minister of Canada]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectIs Context triple: [statue of William Lyon Mackenzie King, subjectIs, longest-serving prime minister of Canada]
-
A.
subjectCanBe
Indicates that the subject has the potential or capability to assume, become, or be classified as the specified object or state.
-
B.
subjectType
chosen
Indicates the classification or category that defines what kind of entity the subject is.
-
C.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
D.
subjectImpliedAs
Indicates that the subject of an action or statement is not explicitly stated but is understood or inferred from context.
-
E.
subjectOfWork
Indicates that one entity is the main topic, focus, or theme that a particular work (such as a book, article, or artwork) is about.
- F. None of above.
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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03345ca0c819081c81148d054fed2 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:55 p.m.