Triple
T816075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyPy |
E17653
|
entity |
| Predicate | hasUseCase |
P19962
|
FINISHED |
| Object | speeding up pure Python code |
—
|
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: speeding up pure Python code | Statement: [PyPy, hasUseCase, speeding up pure Python code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUseCase Context triple: [PyPy, hasUseCase, speeding up pure Python code]
-
A.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
B.
canUse
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
C.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
D.
usedCapability
Indicates that an entity employed or exercised a particular capability, skill, or function in performing an action or achieving a result.
-
E.
hasApp
Indicates that an entity possesses, provides, or is associated with a particular application.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab5157b08190b6c8f2fd455f261e |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa756920819080ae82948974c876 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4781c88190ae36906251347cdc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.