Triple
T8816479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santos Dumont Airport |
E209788
|
entity |
| Predicate | hasScenicApproach |
P9193
|
FINISHED |
| Object | views of Rio de Janeiro landmarks |
—
|
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: views of Rio de Janeiro landmarks | Statement: [Santos Dumont Airport, hasScenicApproach, views of Rio de Janeiro landmarks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenicApproach Context triple: [Santos Dumont Airport, hasScenicApproach, views of Rio de Janeiro landmarks]
-
A.
hasScenicDrive
Indicates that one entity offers or features a visually appealing or picturesque driving route associated with it.
-
B.
hasScenicSections
Indicates that a route, path, or area contains segments that are visually attractive or offer notable scenic views.
-
C.
hasScenicViewOf
chosen
Indicates that one entity offers a visually appealing or picturesque view of another entity.
-
D.
hasScenicValue
Indicates that something possesses notable aesthetic or visual appeal, often due to its natural beauty or pleasing surroundings.
-
E.
isPartOfScenicVista
Indicates that something is included within, or contributes to, a larger scenic vista or panoramic view.
- 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_69ca8364e13081909c85fe80f44fe86f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc600bd8a88190ad891a96201d796b |
completed | April 1, 2026, midnight |
| PD | Predicate disambiguation | batch_69cc5c21e64c81908490e3b0875dc0d6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:46 p.m.