Triple
T400547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C |
E9269
|
entity |
| Predicate | memoryModel |
P12985
|
FINISHED |
| Object | manual allocation and deallocation |
—
|
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: manual allocation and deallocation | Statement: [C, memoryModel, manual allocation and deallocation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memoryModel Context triple: [C, memoryModel, manual allocation and deallocation]
-
A.
memoryType
Indicates the specific category or kind of memory associated with an entity or process.
-
B.
primaryMemoryType
Indicates the main or dominant type of memory associated with or used by an entity in a given context.
-
C.
executionModel
Indicates how a process, task, or operation is carried out or implemented, specifying the underlying method, strategy, or mechanism of its execution.
-
D.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
E.
hasRAM
Indicates that an entity possesses or is equipped with a specified amount or type of random-access memory (RAM).
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ec8e655c819081eff85c0ef55fa5 |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96ee4ec8190a5c0e3f491d3963d |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2eb7c56bc8190ab787801af2eec8d |
completed | Feb. 28, 2026, 1:19 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.