Triple
T2485216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pella Lifestyle Series |
E55909
|
entity |
| Predicate | hasWarranty |
P22687
|
FINISHED |
| Object | limited warranty (terms vary by product and region) |
—
|
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: limited warranty (terms vary by product and region) | Statement: [Pella Lifestyle Series, hasWarranty, limited warranty (terms vary by product and region)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWarranty Context triple: [Pella Lifestyle Series, hasWarranty, limited warranty (terms vary by product and region)]
-
A.
warrantyType
chosen
Indicates the specific category or kind of warranty associated with a product, service, or agreement.
-
B.
warrantyClause
Indicates that a contractual provision defines the scope, conditions, and duration of a warranty obligation between parties.
-
C.
wearerEntitlement
Indicates that an entity has the right or authorization to wear a particular item (such as clothing, equipment, or an accessory).
-
D.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
hasFair
Indicates that an entity organizes, hosts, or is associated with a fair or similar event.
- 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_69ab49e670a88190b928e08302381710 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd20b6d008190acec0eb172e218c9 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b7cf088190bcff4dac6150044c |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.