Triple
T17175271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calgary Transit |
E416842
|
entity |
| Predicate | hasRouteBrand |
P11989
|
FINISHED |
| Object | MAX bus rapid transit routes |
—
|
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: MAX bus rapid transit routes | Statement: [Calgary Transit, hasRouteBrand, MAX bus rapid transit routes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRouteBrand Context triple: [Calgary Transit, hasRouteBrand, MAX bus rapid transit routes]
-
A.
hasBrandType
Indicates that an entity is associated with or categorized under a particular brand type or classification.
-
B.
hasBrandName
Indicates that an entity is associated with or identified by a specific brand name.
-
C.
hasBrandRole
Indicates that an entity holds a specific functional or organizational role in relation to a particular brand.
-
D.
hasBrandConcept
Indicates that an entity is associated with or embodies a particular brand concept or branding idea.
-
E.
hasBranding
chosen
Indicates that one entity carries, displays, or is associated with the brand identity of another entity.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0cec448190b30466628a2ff23f |
completed | April 18, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:37 a.m.