Triple
T11518758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloor–Yonge intersection |
E273100
|
entity |
| Predicate | hasNearbyBuildingType |
P50464
|
FINISHED |
| Object | condominium towers |
—
|
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: condominium towers | Statement: [Bloor–Yonge intersection, hasNearbyBuildingType, condominium towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyBuildingType Context triple: [Bloor–Yonge intersection, hasNearbyBuildingType, condominium towers]
-
A.
hasNeighboringBuilding
Indicates that one building is located adjacent to or directly next to another building.
-
B.
hasNearbyCivicBuilding
Indicates that one entity is located close to, or in the immediate vicinity of, a civic building such as a government, public service, or community facility.
-
C.
hasMainBuildingNear
Indicates that the primary or central building associated with an entity is located in close physical proximity to another specified entity or place.
-
D.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
E.
containsBuildingType
chosen
Indicates that a location or area includes at least one building of the specified type.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fcf927081908ef89eff7ad833b0 |
completed | April 10, 2026, 4:42 a.m. |
| PD | Predicate disambiguation | batch_69d80876e5f0819088cff2e72f773cf6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.