Triple
T2016802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Howe Island |
E44012
|
entity |
| Predicate | distanceToPortMacquarieKilometres |
P34514
|
FINISHED |
| Object | about 780 |
—
|
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: about 780 | Statement: [Lord Howe Island, distanceToPortMacquarieKilometres, about 780]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPortMacquarieKilometres Context triple: [Lord Howe Island, distanceToPortMacquarieKilometres, about 780]
-
A.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
-
B.
distanceFromBrisbane
Indicates the measured distance between a given location or entity and the city of Brisbane.
-
C.
distanceFromCanberra
Indicates the spatial distance between a given location and the city of Canberra.
-
D.
distanceToMelbourne
Indicates the spatial distance between a given location or entity and the city of Melbourne.
-
E.
distanceToAdelaide_km
Indicates the physical distance, measured in kilometers, between a given location and Adelaide.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8ccdb7c81909f6b3c96f79fcdfc |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a03a1c81909ad50d56667db2d5 |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb83e7888819096dc40275c77daff |
completed | March 7, 2026, 5:31 a.m. |
Created at: March 4, 2026, 7:38 p.m.