Triple
T21179409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Badung |
E521904
|
entity |
| Predicate | tourismPosition |
P143179
|
FINISHED |
| Object | away from Bali’s main southern hubs |
—
|
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: away from Bali’s main southern hubs | Statement: [North Badung, tourismPosition, away from Bali’s main southern hubs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismPosition Context triple: [North Badung, tourismPosition, away from Bali’s main southern hubs]
-
A.
tourismModel
Indicates the conceptual framework or approach that defines how tourism activities, services, and interactions are structured and operate within a given context.
-
B.
tourismFrom
Indicates that tourists or visitor activity originates from one place and is directed toward another location.
-
C.
tourismTrend
Indicates how patterns or levels of tourism activity change over time or across locations.
-
D.
tourismFeature
Indicates that something serves as an attraction, amenity, or point of interest relevant to tourism or visitors.
-
E.
tourismBoom
Indicates a rapid and significant increase in tourism activity, such as visitor numbers, spending, or development, within a particular place or period.
- 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_69e0b50ef1d48190b063aa342667df22 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7301c842c8190b969a8b3f194003a |
completed | April 21, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69e5f6027c248190a170a36612bd337e |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5f993240c8190847c0b08e65726c8 |
completed | April 20, 2026, 10:01 a.m. |
Created at: April 16, 2026, 3:01 p.m.