Triple
T34409876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auckland–Sydney |
E883225
|
entity |
| Predicate | isOneOfBusiestRoutesIn |
P181191
|
FINISHED |
| Object | Australasia |
—
|
NE NERFINISHED |
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: Australasia | Statement: [Auckland–Sydney, isOneOfBusiestRoutesIn, Australasia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOneOfBusiestRoutesIn Context triple: [Auckland–Sydney, isOneOfBusiestRoutesIn, Australasia]
-
A.
isOneOfBusiestStopsOn
Indicates that a stop ranks among the most heavily used or frequently served stops on a given route or line.
-
B.
isMostPopularRouteOn
Indicates that a particular route is the most frequently chosen or favored option on a given transportation line, network, or service.
-
C.
oneOfBusiestStationsIn
Indicates that a station is among the busiest stations within a specified area or system.
-
D.
isMostCommonRouteTo
Indicates that one route is the most frequently used or typical way to reach a particular destination or outcome compared to all other possible routes.
-
E.
isOneOfBusiestAreasIn
Indicates that an area ranks among the most heavily used, active, or trafficked locations within a specified larger place or region.
- 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_69f349c1f2208190a09a489bb8b2719d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7626667f48190ad90867eb67ec582 |
completed | May 3, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f76175d6608190b60b268e20f49ed9 |
completed | May 3, 2026, 2:53 p.m. |
| PDg | Predicate description generation | batch_69f762651e088190baa21f25378a6065 |
completed | May 3, 2026, 2:57 p.m. |
Created at: May 1, 2026, 1:59 a.m.