Triple
T556571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parliament House, Canberra |
E11953
|
entity |
| Predicate | visitorCapacityPerYear |
P12597
|
FINISHED |
| Object | over 1 million visitors |
—
|
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: over 1 million visitors | Statement: [Parliament House, Canberra, visitorCapacityPerYear, over 1 million visitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorCapacityPerYear Context triple: [Parliament House, Canberra, visitorCapacityPerYear, over 1 million visitors]
-
A.
annualCapacity
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
-
B.
touristArrivalsPerYearApprox
chosen
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
C.
visitorCount
Indicates the number of visitors associated with a particular entity, context, or time period.
-
D.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
E.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4991ef9b0819092ec0407270373f4 |
completed | March 1, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69a494bd78e8819083c519669158f209 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.