Triple
T38450942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Arboretum Canberra |
E912172
|
entity |
| Predicate | numberOfForests |
P204659
|
FINISHED |
| Object | approximately 94 |
—
|
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: approximately 94 | Statement: [National Arboretum Canberra, numberOfForests, approximately 94]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfForests Context triple: [National Arboretum Canberra, numberOfForests, approximately 94]
-
A.
numberOfTrees
Indicates the count or quantity of trees associated with a given entity or context.
-
B.
isForested
Indicates that an area or region is covered predominantly by forest or dense tree vegetation.
-
C.
forestArea
Indicates the extent or size of land covered by forest within a given area or region.
-
D.
hasForestType
Indicates that an area or location is characterized by a specific type or classification of forest.
-
E.
forestDistrict
Indicates that one entity functions as the forest district or forest management administrative unit responsible for the other entity.
- 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_69f76e84e2dc81908badf05b3aafa9ea |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:31 p.m.