Triple
T7559032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koreatown (Bloor between Bathurst and Christie) |
E178746
|
entity |
| Predicate | businessTypeConcentration |
P35312
|
FINISHED |
| Object | independent restaurants |
—
|
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: independent restaurants | Statement: [Koreatown (Bloor between Bathurst and Christie), businessTypeConcentration, independent restaurants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessTypeConcentration Context triple: [Koreatown (Bloor between Bathurst and Christie), businessTypeConcentration, independent restaurants]
-
A.
businessBase
Indicates that one entity serves as the primary business foundation, core location, or main operational base for another entity.
-
B.
notableBusinessType
chosen
Indicates that an entity is notably associated with, characterized by, or best known for a particular type of business.
-
C.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
D.
concernsBusinessType
Indicates that something is related or applicable to a particular type or category of business.
-
E.
sectorStrength
Indicates the relative performance or influence level of a specific sector compared to others within a broader system or market.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8dc7d288190a0d08ba704cc3fc2 |
completed | March 27, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69c6f4dc485c819080da13e3b7f4f08f |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:50 p.m.