Triple
T31776280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SLC (single-level cell) |
E811072
|
entity |
| Predicate | hasDataRetention |
P166721
|
FINISHED |
| Object | long data retention |
—
|
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: long data retention | Statement: [SLC (single-level cell), hasDataRetention, long data retention]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDataRetention Context triple: [SLC (single-level cell), hasDataRetention, long data retention]
-
A.
dataRetentionPeriod
Indicates the length of time data is stored or kept before it is deleted, archived, or otherwise disposed of.
-
B.
dataRetentionType
chosen
Indicates the manner or policy by which data is retained, such as how long and under what conditions it is stored or preserved.
-
C.
retentionMethod
Indicates the method or strategy used to retain or keep something (such as data, customers, or resources) over time.
-
D.
performanceRetention
Indicates how well a previously achieved level of performance is maintained over time or across repeated instances.
-
E.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
- 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_69f348e544a48190ab6e700b05f6438c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6bbbef7a88190b0affdec1d41c1e0 |
completed | May 3, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6cef208190bc5cd43d96127004 |
completed | May 3, 2026, 3:01 a.m. |
Created at: April 30, 2026, 11:35 p.m.