Triple
T38084081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luther Country |
E950927
|
entity |
| Predicate | tourismMarketedTo |
P190244
|
FINISHED |
| Object | international 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: international visitors | Statement: [Luther Country, tourismMarketedTo, international visitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismMarketedTo Context triple: [Luther Country, tourismMarketedTo, international visitors]
-
A.
tourismMarket
Indicates a relationship where a location, service, or product functions as a destination or offering within the travel and tourism economy, attracting and serving tourists as a market segment.
-
B.
hasTourismMarketing
Indicates that an entity engages in or is associated with activities, strategies, or efforts aimed at promoting tourism.
-
C.
tourismTrend
Indicates how patterns or levels of tourism activity change over time or across locations.
-
D.
travelMarket
Indicates a relationship where an entity participates in or is associated with the commercial exchange, promotion, or sale of travel-related services or experiences.
-
E.
tourismFrom
Indicates that tourists or visitor activity originates from one place and is directed toward another location.
- 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_69f76f03a3608190a73fd6df87c792a8 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcc42cbac48190b8d3e4c9ce140838 |
completed | May 7, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69fcb0fc69c88190800453eb57a7e62c |
completed | May 7, 2026, 3:34 p.m. |
| PDg | Predicate description generation | batch_69fcc42b9334819099929649b7ef68ea |
completed | May 7, 2026, 4:56 p.m. |
Created at: May 3, 2026, 4:21 p.m.