Triple
T4466616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France and Italy |
E98393
|
entity |
| Predicate | shareTourismFlows |
P56675
|
FINISHED |
| Object | high bilateral tourism |
—
|
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: high bilateral tourism | Statement: [France and Italy, shareTourismFlows, high bilateral tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shareTourismFlows Context triple: [France and Italy, shareTourismFlows, high bilateral tourism]
-
A.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
-
B.
tourismBoom
Indicates a rapid and significant increase in tourism activity, such as visitor numbers, spending, or development, within a particular place or period.
-
C.
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.
-
D.
seasonalTourism
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
E.
touristArrivalsRank
Indicates the relative position of a place compared to others based on the number of tourists arriving there.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356fb69a0819099f0005779f4fcac |
completed | March 13, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69b3563bf4f8819081726cde3a34460b |
completed | March 13, 2026, 12:11 a.m. |
| PDg | Predicate description generation | batch_69b356f9afc48190acb50c45a310e072 |
completed | March 13, 2026, 12:14 a.m. |
Created at: March 12, 2026, 11:34 p.m.