Triple
T2820638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pella Designer Series |
E54798
|
entity |
| Predicate | offersStyle |
P9404
|
FINISHED |
| Object | casement windows |
—
|
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: casement windows | Statement: [Pella Designer Series, offersStyle, casement windows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersStyle Context triple: [Pella Designer Series, offersStyle, casement windows]
-
A.
offersFeature
chosen
Indicates that one entity provides or makes available a particular feature or capability to another entity.
-
B.
offersEdition
Indicates that one entity provides or makes available a particular version or edition of another entity.
-
C.
offersServiceTo
Indicates that one entity provides or makes a service available for the benefit or use of another entity.
-
D.
offersProgram
Indicates that an entity provides or makes available a specific program (such as a course, curriculum, or initiative).
-
E.
offersGrade
Indicates that one entity assigns or provides an academic grade or evaluation to another entity.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.