Triple
T369902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Constitution Act, 1867 |
E8244
|
entity |
| Predicate | sectionCountApprox |
P1632
|
FINISHED |
| Object | 147 sections (as amended) |
—
|
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: 147 sections (as amended) | Statement: [Constitution Act, 1867, sectionCountApprox, 147 sections (as amended)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectionCountApprox Context triple: [Constitution Act, 1867, sectionCountApprox, 147 sections (as amended)]
-
A.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
B.
approximateNumberOfVerses
Indicates an estimated or approximate count of verses associated with an entity.
-
C.
hasSectionCount
chosen
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
D.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
-
E.
branchCount
Indicates the number of branches associated with a given entity or structure.
- 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebfdb0608190b1794a871d0d237a |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e960d880819084b3df4e5137a1e2 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.