Triple
T81084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westminster Abbey |
E1627
|
entity |
| Predicate | visitorAttraction |
P530
|
FINISHED |
| Object | millions of visitors per year |
—
|
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: millions of visitors per year | Statement: [Westminster Abbey, visitorAttraction, millions of visitors per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorAttraction Context triple: [Westminster Abbey, visitorAttraction, millions of visitors per year]
-
A.
visitorCenter
Indicates that a location serves as a visitor center for a place, providing information or services to visitors of that place.
-
B.
isTouristDestination
chosen
Indicates that a place is recognized as a location people commonly visit for leisure, sightseeing, or travel.
-
C.
museumCity
Indicates the city in which a given museum is located.
-
D.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
E.
majorPark
Indicates that a park is classified as a major or primary park within a given area or system.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25053ca208190a371b0d38000c2b9 |
completed | Feb. 28, 2026, 2:17 a.m. |
| PD | Predicate disambiguation | batch_69a24eb2998c819082681da74601d446 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.