Triple
T22882096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Boroondara |
E567501
|
entity |
| Predicate | hasParksAndGardens |
P22590
|
FINISHED |
| Object | extensive tree canopy |
—
|
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 tree canopy | Statement: [City of Boroondara, hasParksAndGardens, extensive tree canopy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParksAndGardens Context triple: [City of Boroondara, hasParksAndGardens, extensive tree canopy]
-
A.
hasParks
chosen
Indicates that one entity possesses, contains, or is associated with one or more parks.
-
B.
hasParkAndGardenRegister
Indicates that an entity is recorded in an official register of parks and gardens.
-
C.
hasBotanicalGarden
Indicates that one entity possesses, contains, or includes a botanical garden as part of its facilities or domain.
-
D.
hasGreenSpaces
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
E.
hasNumberOfParks
Indicates the quantity of parks associated with 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f5cd98c8190b3c31ffd4c066331 |
completed | April 29, 2026, 3:47 a.m. |
| PD | Predicate disambiguation | batch_69ef3b6b2e2481908258156937b5a745 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:39 p.m.