Triple
T3221063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Pass |
E67511
|
entity |
| Predicate | distanceToCanberraByRoad |
P16348
|
FINISHED |
| Object | approximately 220 kilometres |
—
|
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 220 kilometres | Statement: [Charlotte Pass, distanceToCanberraByRoad, approximately 220 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCanberraByRoad Context triple: [Charlotte Pass, distanceToCanberraByRoad, approximately 220 kilometres]
-
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.
distanceFromDarwin
Indicates the spatial distance between an entity and the location Darwin.
-
D.
distanceToMelbourne
Indicates the spatial distance between a given location or entity and the city of Melbourne.
-
E.
distanceToDarwin
Indicates the spatial distance between a given entity’s location and the location of Darwin.
- 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_69ad858b8adc8190ad989712c87a476b |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adae16f20081909d7f3bac016f961d |
completed | March 8, 2026, 5:12 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0bb6c48190a0659c67d40ee37c |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:08 p.m.