Triple
T8663082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woluwe-Saint-Lambert |
E205594
|
entity |
| Predicate | greenAreaLevel |
P45219
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Woluwe-Saint-Lambert, greenAreaLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: greenAreaLevel Context triple: [Woluwe-Saint-Lambert, greenAreaLevel, high]
-
A.
isGreenSpaceFor
Indicates that one entity serves as a designated green or open space intended for use or benefit by another entity.
-
B.
hasGreenSpaces
chosen
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
C.
greenRoofArea
Indicates the area or surface size of a roof that is covered with vegetation or green roofing systems.
-
D.
isGreenSpaceType
Indicates that one entity is classified as a type or category of green space (such as parks, gardens, or natural vegetated areas) in relation to another.
-
E.
grassColor
Indicates the color attribute associated with a grass entity.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4872a190819087d679f3006bd030 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.