Triple
T11864905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warm Home Discount |
E282255
|
entity |
| Predicate | typicalApplicationWindow |
P101953
|
FINISHED |
| Object | autumn and winter |
—
|
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: autumn and winter | Statement: [Warm Home Discount, typicalApplicationWindow, autumn and winter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalApplicationWindow Context triple: [Warm Home Discount, typicalApplicationWindow, autumn and winter]
-
A.
typicalWindowManagers
Indicates that the subject is associated with or characterized by commonly used or standard window managers.
-
B.
windowType
Indicates the specific kind or category of window associated with an entity.
-
C.
windowManagement
Indicates the relationship or action of controlling, arranging, or interacting with on-screen windows within a graphical user interface.
-
D.
exampleApplication
Indicates that something serves as a representative or illustrative instance of how an application is used or functions.
-
E.
typicalMonitor
Indicates that one entity commonly or characteristically monitors, observes, or oversees another entity in a usual or expected manner.
- F. None of above. chosen
Provenance (4 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a73883508190a78b5f4ba4a220df |
completed | April 10, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69d8a2589f0c8190ad82ff11acabae93 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:43 p.m.