Triple
T28390686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aero is not supported |
E719148
|
entity |
| Predicate | relatedToFeature |
P201453
|
FINISHED |
| Object | Windows Aero |
—
|
NE NERFINISHED |
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: Windows Aero | Statement: [Aero is not supported, relatedToFeature, Windows Aero]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToFeature Context triple: [Aero is not supported, relatedToFeature, Windows Aero]
-
A.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
B.
relatedPass
Indicates that one pass is associated with or connected to another pass in some relevant way.
-
C.
relatedToProduct
Indicates a general association or connection between an entity and a specific product, without specifying the exact nature of that relationship.
-
D.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
E.
relatedToTerm
Indicates a general, non-specific relationship or association between one term and another.
- 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_69eff6ef211081909d31d9be5f5567e6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69fff86e544c81908063f61b876c9d78 |
completed | May 10, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fff7e7cb688190977eeca41aad25b9 |
completed | May 10, 2026, 3:13 a.m. |
| PDg | Predicate description generation | batch_69fff86da6f48190aac10a80b648c05a |
completed | May 10, 2026, 3:15 a.m. |
Created at: April 28, 2026, 1:13 a.m.