Triple
T17674850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BEAM virtual machine |
E440619
|
entity |
| Predicate | garbageCollectionType |
P16210
|
FINISHED |
| Object | incremental garbage collection |
—
|
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: incremental garbage collection | Statement: [BEAM virtual machine, garbageCollectionType, incremental garbage collection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: garbageCollectionType Context triple: [BEAM virtual machine, garbageCollectionType, incremental garbage collection]
-
A.
usesGarbageCollectorType
chosen
Indicates that one entity employs or is configured to employ a specific type of garbage collection mechanism or strategy associated with another entity.
-
B.
isGarbageCollected
Indicates that an object or resource is automatically reclaimed by a garbage collector when it is no longer reachable or needed.
-
C.
decompositionType
Indicates the specific way in which a whole is broken down into its constituent parts or components.
-
D.
pruningType
Indicates the specific method or style of pruning applied to an entity (e.g., how it is cut back or trimmed).
-
E.
compressorType
Indicates the specific kind or category of compressor associated with an entity.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6ba22081909e2099490c047378 |
completed | April 19, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 10 a.m.