Triple
T23989342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CQL |
E605019
|
entity |
| Predicate | hasBooleanOperatorExample |
P1259
|
FINISHED |
| Object | and |
—
|
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: and | Statement: [CQL, hasBooleanOperatorExample, and]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBooleanOperatorExample Context triple: [CQL, hasBooleanOperatorExample, and]
-
A.
supportsBooleanOperators
Indicates that the subject allows the use of Boolean operators (such as AND, OR, NOT) within its operations or expressions.
-
B.
hasComplementaryOperator
Indicates that one operator is associated with another operator that performs a complementary or inverse function to it.
-
C.
usesOperatorPosition
Indicates that one entity occupies or is assigned to the role of an operator within a particular position or context.
-
D.
involvesOperator
Indicates that a given process, action, or relationship includes or makes use of a specific operator as a participating element.
-
E.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
- 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_69e295463f7c8190b1c19dbd114641b9 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d38a40588190887c2abc6565bbf4 |
completed | April 29, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69f1615994c48190a5de95d3f7e5cd0a |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:37 p.m.