Triple
T9500223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Dupont neighborhood |
E229115
|
entity |
| Predicate | greenSpaceAccess |
P33602
|
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: [Fort Dupont neighborhood, greenSpaceAccess, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: greenSpaceAccess Context triple: [Fort Dupont neighborhood, greenSpaceAccess, high]
-
A.
hasNearbyGreenSpace
chosen
Indicates that an entity is located close to an area of green space, such as a park, garden, or natural vegetation.
-
B.
isGreenSpaceFor
Indicates that one entity serves as a designated green or open space intended for use or benefit by another entity.
-
C.
hasGreenSpaces
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
D.
isUrbanForest
Indicates that an area of trees and vegetation is located within or closely integrated with an urban or suburban environment.
-
E.
isUrbanOasisFor
Indicates a place serves as a refreshing, nature-rich retreat or sanctuary within an otherwise urban or heavily built-up environment for a given 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd983c308c8190bde6858ac1ca8ea5 |
completed | April 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cca5651a588190a3cfebe249a223e5 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:56 p.m.