Triple
T816061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyPy |
E17653
|
entity |
| Predicate | hasRepositoryMirror |
P19960
|
FINISHED |
| Object | https://github.com/pypy/pypy |
—
|
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: https://github.com/pypy/pypy | Statement: [PyPy, hasRepositoryMirror, https://github.com/pypy/pypy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRepositoryMirror Context triple: [PyPy, hasRepositoryMirror, https://github.com/pypy/pypy]
-
A.
hasSecondaryMirrorPosition
Indicates the spatial placement or configuration of a secondary mirror relative to the primary optical system.
-
B.
primaryMirrorConfiguration
Indicates the specific structural and optical setup used for a system’s primary mirror.
-
C.
mirrorType
Indicates that one entity is a specific kind or category of mirror in relation to another entity.
-
D.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
-
E.
hasReplicaDisplays
Indicates that an entity features or presents one or more replica items as displays.
- 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.