Triple
T14802448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sutton (New South Wales) |
E347940
|
entity |
| Predicate | distanceToCanberraCBD_km |
P16348
|
FINISHED |
| Object | approximately 18 |
—
|
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 18 | Statement: [Sutton (New South Wales), distanceToCanberraCBD_km, approximately 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCanberraCBD_km Context triple: [Sutton (New South Wales), distanceToCanberraCBD_km, approximately 18]
-
A.
distanceFromCanberra
chosen
Indicates the spatial distance between a given location and the city of Canberra.
-
B.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
-
C.
distanceToBrisbane_km
Indicates the physical distance, measured in kilometers, between a given location and Brisbane.
-
D.
distanceFromBrisbane
Indicates the measured distance between a given location or entity and the city of Brisbane.
-
E.
distanceToMelbourne
Indicates the spatial distance between a given location or entity and the city of Melbourne.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decf30d044819082ac038e06481aab |
completed | April 14, 2026, 11:35 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:33 a.m.