Triple
T272266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Officer of the Order of Canada |
E5658
|
entity |
| Predicate | insigniaIncludes |
P1617
|
FINISHED |
| Object | white enamel snowflake badge |
—
|
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: white enamel snowflake badge | Statement: [Officer of the Order of Canada, insigniaIncludes, white enamel snowflake badge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: insigniaIncludes Context triple: [Officer of the Order of Canada, insigniaIncludes, white enamel snowflake badge]
-
A.
includes
Indicates that one entity contains, encompasses, or has another entity as a part, member, or subset.
-
B.
hasSignage
Indicates that appropriate signs or visual markers are present to convey information, directions, warnings, or identification related to the associated entity.
-
C.
hasTypeOfInsignia
chosen
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
D.
hasINSEECODE
Indicates that an entity is associated with a specific INSEE code, identifying it within the French national statistical and administrative system.
-
E.
alsoUsedIn
Indicates that something is additionally employed, applied, or present in another context, setting, or use case beyond the primary one.
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25e69a9248190b9e7959b43223baa |
completed | Feb. 28, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69a25b721180819080d43c43fcbccf87 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.