Triple
T1621874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giverny garden |
E35048
|
entity |
| Predicate | tourist |
P17187
|
FINISHED |
| Object | major tourist destination |
—
|
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: major tourist destination | Statement: [Giverny garden, tourist, major tourist destination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourist Context triple: [Giverny garden, tourist, major tourist destination]
-
A.
touristAccess
chosen
Indicates that a place or resource is available for use or visitation by tourists.
-
B.
touristGatewayTo
Indicates a relationship where one place serves as the primary access point or entry hub for tourists visiting another place.
-
C.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
D.
coTraveler
Indicates that two or more entities are traveling together along (part of) the same journey or route.
-
E.
tourismFrom
Indicates that tourists or visitor activity originates from one place and is directed toward another location.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.