Triple
T4761526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indian Act |
E105707
|
entity |
| Predicate | BillC31Effect |
P53074
|
FINISHED |
| Object | removed many gender-based status discrimination rules |
—
|
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: removed many gender-based status discrimination rules | Statement: [Indian Act, BillC31Effect, removed many gender-based status discrimination rules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: BillC31Effect Context triple: [Indian Act, BillC31Effect, removed many gender-based status discrimination rules]
-
A.
eventEffect
chosen
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
B.
section3Effect
Indicates the effect, consequence, or outcome that arises specifically from the application or enforcement of section 3 of a given law, policy, or document.
-
C.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
D.
tookEffect
Indicates that a change, rule, condition, or event became active, operative, or started producing its intended consequences.
-
E.
budgetaryEffect
Indicates the financial impact or change in budget resulting from a particular action, decision, or policy.
- 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_69bd43f14cac819081c7c69803648211 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd650eefe08190b99f9f01b121dbfd |
completed | March 20, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69bd6225c9488190afee5bb3619d0365 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:20 p.m.