Triple
T26446828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pearson correlation coefficient |
E665236
|
entity |
| Predicate | interpretationAt1 |
P67724
|
FINISHED |
| Object | perfect positive linear relationship |
—
|
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: perfect positive linear relationship | Statement: [Pearson correlation coefficient, interpretationAt1, perfect positive linear relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interpretationAt1 Context triple: [Pearson correlation coefficient, interpretationAt1, perfect positive linear relationship]
-
A.
intendedInterpretation
chosen
Indicates that one entity is meant to be understood or interpreted in a particular way, sense, or meaning relative to another.
-
B.
interpretationMethod
Indicates the method, technique, or process used to interpret or derive meaning from something.
-
C.
containsInterpretationOf
Indicates that one entity includes or embodies an interpretation or understanding of another entity.
-
D.
interpretationFacility
Indicates that an entity serves as a facility or venue where interpretation (such as language or translation services) is provided or supported.
-
E.
HnInterpretation
Indicates that one entity serves as an interpretation, explanation, or semantic mapping of another entity (such as a text, symbol, or data).
- 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_69ee883d5040819097dd154643005230 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f6978fe97081908fe568091ad9b159 |
completed | May 3, 2026, 12:32 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 27, 2026, 12:02 a.m.