Triple
T32122898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Set (Swift) |
E820426
|
entity |
| Predicate | literalSyntaxExample |
P30248
|
FINISHED |
| Object | let s: Set<Int> = [1, 2, 3] |
—
|
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: let s: Set<Int> = [1, 2, 3] | Statement: [Set (Swift), literalSyntaxExample, let s: Set<Int> = [1, 2, 3]]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literalSyntaxExample Context triple: [Set (Swift), literalSyntaxExample, let s: Set<Int> = [1, 2, 3]]
-
A.
lineExample
Indicates that one entity serves as an illustrative or representative example of a particular line, sequence, or linear construct associated with another entity.
-
B.
definesSyntax
Indicates that one entity specifies or determines the formal structure, rules, or grammar by which another entity is expressed or interpreted.
-
C.
syntaxStyle
Indicates the stylistic or structural conventions used in the form or arrangement of an expression, statement, or code.
-
D.
codeExample
chosen
Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
-
E.
tailcodeExample
Indicates that a specific tailcode is provided as an example instance or illustration of that tailcode designation.
- 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_69f34902d42c819083a8e6bba9a8bb9a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: May 1, 2026, 12:28 a.m.