Triple
T34274934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beuvron-en-Auge |
E879424
|
entity |
| Predicate | tourismLabel |
P84159
|
FINISHED |
| Object | Village Fleuri |
—
|
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: Village Fleuri | Statement: [Beuvron-en-Auge, tourismLabel, Village Fleuri]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismLabel Context triple: [Beuvron-en-Auge, tourismLabel, Village Fleuri]
-
A.
tourismFeature
Indicates that something serves as an attraction, amenity, or point of interest relevant to tourism or visitors.
-
B.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
C.
tourismTheme
Indicates the main subject or focus of a tourism-related activity, service, or destination (such as cultural, adventure, or eco-tourism).
-
D.
tourismFrom
Indicates that tourists or visitor activity originates from one place and is directed toward another location.
-
E.
tourismCharacteristic
chosen
Indicates that something has a specific feature, quality, or attribute relevant to tourism, such as what makes a place, service, or activity notable or suitable for tourists.
- 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:56 a.m.