Triple
T22557952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Germany and Italy |
E557735
|
entity |
| Predicate | haveSignificantTouristFlowsBetweenThem |
P148652
|
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: [Germany and Italy, haveSignificantTouristFlowsBetweenThem, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveSignificantTouristFlowsBetweenThem Context triple: [Germany and Italy, haveSignificantTouristFlowsBetweenThem, true]
-
A.
hasCityPair
Indicates a relationship that links two cities considered as a connected or associated pair, often for purposes such as travel, trade, or comparison.
-
B.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
-
C.
betweenCity
Indicates a spatial relationship where one entity is located in the area or position separating two specified cities.
-
D.
hasMajorCityOnRoute
Indicates that a major city lies along, or is directly served by, a specified route or path between locations.
-
E.
areMajorTouristDestinations
Indicates that the referenced places are widely recognized and frequently visited as primary tourist destinations.
- 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f7b06e08190b3ca82a783965942 |
completed | April 29, 2026, 1:31 a.m. |
| PD | Predicate disambiguation | batch_69e898cb3fb48190add6ab24a2df5822 |
completed | April 22, 2026, 9:45 a.m. |
| PDg | Predicate description generation | batch_69e8aa3b4c288190951cca06d42bea51 |
completed | April 22, 2026, 11 a.m. |
Created at: April 16, 2026, 8:52 p.m.