Triple
T32258045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | μ-calculus |
E824073
|
entity |
| Predicate | semanticsUses |
P74447
|
FINISHED |
| Object | monotone operators on power sets |
—
|
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: monotone operators on power sets | Statement: [μ-calculus, semanticsUses, monotone operators on power sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: semanticsUses Context triple: [μ-calculus, semanticsUses, monotone operators on power sets]
-
A.
definitionUses
Indicates that one definition makes use of, depends on, or references another definition.
-
B.
symbolicallyUses
Indicates that one entity employs another as a symbol or representation to convey meaning, ideas, or associations rather than for its literal or practical function.
-
C.
definesUseOf
Indicates that one entity specifies or determines how another entity is to be used or applied.
-
D.
hasSemanticsDefinedBy
chosen
Indicates that the meaning or interpretation of one entity is specified, constrained, or determined by another entity.
-
E.
useOfTerm
Indicates that one entity employs or applies a specific term or expression in reference to another entity or context.
- 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_69f3490db0748190bfef6e50c95d39d3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bc5597d88190829174f5f7ec6148 |
completed | May 3, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:41 a.m.