Triple
T19846009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scientia est potentia |
E476860
|
entity |
| Predicate | subjectTerm |
P450
|
FINISHED |
| Object | scientia |
—
|
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: scientia | Statement: [Scientia est potentia, subjectTerm, scientia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectTerm Context triple: [Scientia est potentia, subjectTerm, scientia]
-
A.
hasSubjectThesaurus
Indicates that an entity is associated with or described using a particular subject thesaurus or controlled subject vocabulary.
-
B.
classificationTerm
Indicates that one entity serves as a categorical label or type used to classify or group another entity.
-
C.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
D.
subjectKey
Indicates that the subject serves as a unique key or identifier used to reference or distinguish an entity in a relationship or dataset.
-
E.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658091c608190b4eb9bcedd88e147 |
completed | April 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.