Triple
T4279776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BI Engine |
E97119
|
entity |
| Predicate | persistenceModel |
P55184
|
FINISHED |
| Object | caches query-relevant data in memory |
—
|
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: caches query-relevant data in memory | Statement: [BI Engine, persistenceModel, caches query-relevant data in memory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: persistenceModel Context triple: [BI Engine, persistenceModel, caches query-relevant data in memory]
-
A.
persistence
Indicates a continued or repeated existence, occurrence, or effort of something over time despite potential changes or obstacles.
-
B.
persistsAfter
Indicates that one state, condition, or effect continues to exist after a specified event, time point, or other state has occurred or ended.
-
C.
supportsPersistence
Indicates that one entity enables or provides the capability for another entity’s data or state to be stored and retained over time.
-
D.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
E.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350367da48190b735deef9b5d2d2e |
completed | March 12, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69b347fc4c0c8190a7fcd814e27308a5 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e0606488190baadf469a1afc3c2 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:07 p.m.