Triple
T19481424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matten bei Interlaken |
E487392
|
entity |
| Predicate | hasTourismAsEconomicActivity |
P33826
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Matten bei Interlaken, hasTourismAsEconomicActivity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTourismAsEconomicActivity Context triple: [Matten bei Interlaken, hasTourismAsEconomicActivity, true]
-
A.
hasTourismAsMainEconomicActivity
Indicates that tourism is the primary source of economic activity or income for the referenced entity.
-
B.
hasTourismIndustry
chosen
Indicates that a place or region possesses an established tourism industry, involving organized services and activities catering to visitors and travelers.
-
C.
hadTourismEconomyFor
Indicates that an entity has sustained a tourism-based economy for a specified period or duration.
-
D.
hasTourismFunction
Indicates that an entity serves a role or purpose related to tourism, such as attracting, accommodating, or providing services to tourists.
-
E.
hasTourismResource
Indicates that a place, area, or entity possesses or is associated with a tourism-related resource, attraction, or facility.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e634393b8081909f5e4c38b2f1a9b7 |
completed | April 20, 2026, 2:12 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.