Triple
T1870170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple M1 Pro |
E39014
|
entity |
| Predicate | memoryOnPackage |
P33363
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Apple M1 Pro, memoryOnPackage, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memoryOnPackage Context triple: [Apple M1 Pro, memoryOnPackage, yes]
-
A.
memoryType
Indicates the specific category or kind of memory associated with an entity or process.
-
B.
hasRAM
Indicates that an entity possesses or is equipped with a specified amount or type of random-access memory (RAM).
-
C.
storageRequirement
Indicates the amount or type of storage capacity or resources that an entity needs in order to function or be maintained.
-
D.
minRAM
Indicates that an entity requires at least a specified minimum amount of RAM to function or be considered valid.
-
E.
installedCapacity
Indicates the maximum output or production capability that has been set up or built for a system, facility, or equipment, typically measured under specified conditions.
- 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_69a8862f7074819096afe7fe65e179e9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb0f79fbc819085c54f3189a552d9 |
completed | March 7, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69abafe2b56c81909e13d543982e6e13 |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb0f630e881908cd9491aeaeb4aed |
completed | March 7, 2026, 5 a.m. |
Created at: March 4, 2026, 7:34 p.m.