Triple
T24986858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evergreen Building |
E625333
|
entity |
| Predicate | hasLandscapeIntegration |
P162981
|
FINISHED |
| Object | extensive planted terraces |
—
|
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: extensive planted terraces | Statement: [Evergreen Building, hasLandscapeIntegration, extensive planted terraces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandscapeIntegration Context triple: [Evergreen Building, hasLandscapeIntegration, extensive planted terraces]
-
A.
hasLandscapeUse
Indicates that something is used or intended to be used within a landscape or landscaping context.
-
B.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
C.
supportsLandscapeMode
Indicates that an entity is capable of functioning or being displayed correctly when oriented in landscape mode.
-
D.
hasLandscapeProtection
Indicates that an area or object is subject to legal or formal measures aimed at preserving its landscape or scenic character.
-
E.
isPartOfLandscape
Indicates that something forms a component or feature within a larger landscape or natural environment.
- F. None of above. chosen
Provenance (4 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_69f63639a84c81909d700a539b458b42 |
completed | May 2, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69f63182f1408190bddc1214fcbd6145 |
completed | May 2, 2026, 5:16 p.m. |
| PDg | Predicate description generation | batch_69f6352df6148190bc10772cd40bd7b3 |
completed | May 2, 2026, 5:32 p.m. |
Created at: April 18, 2026, 6:03 a.m.