Triple
T24986859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evergreen Building |
E625333
|
entity |
| Predicate | hasLandscapeIntegration |
P162981
|
FINISHED |
| Object | green roofs |
—
|
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: green roofs | Statement: [Evergreen Building, hasLandscapeIntegration, green roofs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandscapeIntegration Context triple: [Evergreen Building, hasLandscapeIntegration, green roofs]
-
A.
hasLandscapeIntegration
chosen
Indicates that an entity is designed or arranged in a way that it harmoniously fits into, interacts with, or complements its surrounding landscape.
-
B.
hasLandscapeUse
Indicates that something is used or intended to be used within a landscape or landscaping context.
-
C.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
D.
supportsLandscapeMode
Indicates that an entity is capable of functioning or being displayed correctly when oriented in landscape mode.
-
E.
hasLandscapeProtection
Indicates that an area or object is subject to legal or formal measures aimed at preserving its landscape or scenic character.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 18, 2026, 6:03 a.m.