Triple
T22000207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ensemble Montréal |
E543303
|
entity |
| Predicate | languagePolicyStance |
P118086
|
FINISHED |
| Object | supports bilingual character of Montreal |
—
|
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: supports bilingual character of Montreal | Statement: [Ensemble Montréal, languagePolicyStance, supports bilingual character of Montreal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languagePolicyStance Context triple: [Ensemble Montréal, languagePolicyStance, supports bilingual character of Montreal]
-
A.
languagePolicyPosition
chosen
Indicates a stance or viewpoint an entity holds regarding rules, regulations, or practices governing language use.
-
B.
languagePolicyAspect
Indicates an aspect or component of a broader language policy, such as its goals, rules, or implementation measures.
-
C.
languagePolicyType
Indicates the specific category or type of language policy that governs how languages are used, managed, or regulated in a given context.
-
D.
policyStance
Indicates the position or viewpoint an entity holds regarding a specific policy or set of policies.
-
E.
languagePolicyRole
Indicates the role or function an entity has in defining, implementing, or influencing a language 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127699a7881908a80b6e9e33fcc0b |
completed | April 28, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69e6f62dc9d88190ae387f145f9528de |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:20 p.m.