Triple
T34800639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto–Montreal |
E1003208
|
entity |
| Predicate | hasSignificantTourismTravel |
P64169
|
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: [Toronto–Montreal, hasSignificantTourismTravel, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignificantTourismTravel Context triple: [Toronto–Montreal, hasSignificantTourismTravel, true]
-
A.
hasTouristVisits
chosen
Indicates that one entity experiences or records visits from tourists to another entity.
-
B.
hasTourismImpactOn
Indicates that one entity affects or influences the tourism levels, patterns, or attractiveness of another entity.
-
C.
hadTourismEconomyFor
Indicates that an entity has sustained a tourism-based economy for a specified period or duration.
-
D.
hasTourismIndustry
Indicates that a place or region possesses an established tourism industry, involving organized services and activities catering to visitors and travelers.
-
E.
hasCrossBorderTourism
Indicates that there is tourism activity involving travel across national or regional borders between the related entities.
- 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: May 3, 2026, 3:59 p.m.