Triple
T9711367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Savoy Restaurant interior, Helsinki |
E235029
|
entity |
| Predicate | featuresFurnitureModel |
P6895
|
FINISHED |
| Object | Artek furniture |
—
|
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: Artek furniture | Statement: [Savoy Restaurant interior, Helsinki, featuresFurnitureModel, Artek furniture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFurnitureModel Context triple: [Savoy Restaurant interior, Helsinki, featuresFurnitureModel, Artek furniture]
-
A.
featuresDecor
Indicates that one entity includes or showcases the decor elements provided or defined by another entity.
-
B.
featuresItem
Indicates that one entity includes, presents, or highlights another entity as a notable item or component.
-
C.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
D.
furnishingType
chosen
Indicates the type or category of furnishings associated with an entity, such as a property or room.
-
E.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
- 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_69ca84cd8fa0819090a5e243ceb37003 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9e0591208190aa57cc9e2aebafb7 |
completed | April 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69cd03bfeca08190924fca43aaa9c10f |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:19 p.m.