Triple
T38328812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aspire Park |
E1036864
|
entity |
| Predicate | hasLandscapedArea |
P201701
|
FINISHED |
| Object | large green spaces |
—
|
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: large green spaces | Statement: [Aspire Park, hasLandscapedArea, large green spaces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandscapedArea Context triple: [Aspire Park, hasLandscapedArea, large green spaces]
-
A.
hasLandscapeRegion
Indicates that something is located within, associated with, or characterized by a particular landscape region.
-
B.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
C.
hasLandscapeUse
Indicates that something is used or intended to be used within a landscape or landscaping context.
-
D.
isPartOfLandscape
Indicates that something forms a component or feature within a larger landscape or natural environment.
-
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. 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_69f76e1c16fc8190bde982289dd5106b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a001540d4f881908a4688881875e774 |
completed | May 10, 2026, 5:18 a.m. |
| PD | Predicate disambiguation | batch_6a0014a8cdd88190a67a90a2df2c5e18 |
completed | May 10, 2026, 5:16 a.m. |
| PDg | Predicate description generation | batch_6a00153fea20819086ff5888bf725d05 |
completed | May 10, 2026, 5:18 a.m. |
Created at: May 3, 2026, 4:30 p.m.