Triple
T697576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cairns |
E13925
|
entity |
| Predicate | distanceToGreatBarrierReef |
P16760
|
FINISHED |
| Object | approximately 70 km offshore at closest point |
—
|
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 70 km offshore at closest point | Statement: [Cairns, distanceToGreatBarrierReef, approximately 70 km offshore at closest point]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGreatBarrierReef Context triple: [Cairns, distanceToGreatBarrierReef, approximately 70 km offshore at closest point]
-
A.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
-
B.
distanceFromJuanFernandezIslands_km
Indicates the distance, measured in kilometers, between an entity and the Juan Fernández Islands.
-
C.
distanceFromMainland
Indicates the measured spatial separation between a location and the nearest point on the mainland.
-
D.
distanceFromCanberra
Indicates the spatial distance between a given location and the city of Canberra.
-
E.
distanceToSaintHelena
Indicates the measured distance between a given entity and the location of Saint Helena.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c99be48190babc37c397b6a186 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d2586b081908e052cc5ba1d2685 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49dc20880819085fa60dc1851f9dc |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.