Triple
T17104602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barcelona Chair |
E415064
|
entity |
| Predicate | upholsteryType |
P84336
|
FINISHED |
| Object | button-tufted leather cushions |
—
|
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: button-tufted leather cushions | Statement: [Barcelona Chair, upholsteryType, button-tufted leather cushions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: upholsteryType Context triple: [Barcelona Chair, upholsteryType, button-tufted leather cushions]
-
A.
upholsteryOption
Indicates the type or choice of upholstery applied to an item, such as a piece of furniture or vehicle interior.
-
B.
hasSeatMaterial
chosen
Indicates that an entity’s seat is made of, or covered with, a specified material.
-
C.
furnishingType
Indicates the type or category of furnishings associated with an entity, such as a property or room.
-
D.
chairType
Indicates the specific kind or category of chair that an entity is classified as.
-
E.
hasCushionType
Indicates that an entity is associated with or equipped with a specific type of cushion.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2591a881909c5f4f7db47f4d6c |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.