Triple
T2767446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LC2 Petit Modèle |
E61369
|
entity |
| Predicate | backrestType |
P14113
|
FINISHED |
| Object | upholstered cushion |
—
|
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: upholstered cushion | Statement: [LC2 Petit Modèle, backrestType, upholstered cushion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: backrestType Context triple: [LC2 Petit Modèle, backrestType, upholstered cushion]
-
A.
hasBackrestType
chosen
Indicates the specific kind or style of backrest that an object (typically a seat or chair) possesses.
-
B.
hasArmrestType
Indicates the specific style or configuration of armrests associated with an item.
-
C.
seatRecline
Indicates that one entity adjusts or is capable of adjusting the backward tilt or reclining position of a seat relative to another entity or context.
-
D.
chairType
Indicates the specific kind or category of chair that an entity is classified as.
-
E.
architecturalStyleOfSeat
Indicates the architectural style that characterizes a particular seat or seating structure.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfc5e1c8190a5ac2c48d3eaeb0a |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.