Triple
T8288827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DISM |
E193844
|
entity |
| Predicate | canRepair |
P81147
|
FINISHED |
| Object | component store (WinSxS) |
—
|
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: component store (WinSxS) | Statement: [DISM, canRepair, component store (WinSxS)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canRepair Context triple: [DISM, canRepair, component store (WinSxS)]
-
A.
isRepairable
chosen
Indicates that an entity can be restored to proper working condition through repair.
-
B.
repairedIn
Indicates that an item or object underwent repair within a specified location or during a particular time period.
-
C.
haveReconstructionWork
Indicates that an entity is undergoing or is associated with reconstruction or restoration work.
-
D.
hasReconstructionWork
Indicates that an entity is undergoing, has undergone, or is associated with reconstruction or restoration work.
-
E.
canBeCorrectedBy
Indicates that something has the potential to be made accurate, fixed, or improved through the intervention or action of a specified agent or method.
- 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_69ca82e32db481908b72f3804fa71152 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7c98e15c8190ac2a0b2a5ff834c9 |
completed | March 31, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_69cb70b5b5348190b296e0ecec95de60 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:52 p.m.