Triple
T2059521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FE Industrial and Systems |
E45754
|
entity |
| Predicate | testsKnowledgeOf |
P5348
|
FINISHED |
| Object | probability and statistics |
—
|
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: probability and statistics | Statement: [FE Industrial and Systems, testsKnowledgeOf, probability and statistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: testsKnowledgeOf Context triple: [FE Industrial and Systems, testsKnowledgeOf, probability and statistics]
-
A.
usesKnowledgeOf
Indicates that one entity applies or draws upon the knowledge possessed by another entity in performing an action or achieving a result.
-
B.
knows
Indicates that one entity has knowledge or awareness of another entity or piece of information.
-
C.
knowledgeType
Indicates the specific category or nature of knowledge associated with an entity or statement (e.g., factual, procedural, conceptual).
-
D.
viewOnKnowledge
Indicates the perspective, stance, or opinion one entity holds regarding another entity’s knowledge or understanding.
-
E.
tests
chosen
Indicates that one entity examines, evaluates, or checks another entity or condition to determine its properties, performance, or correctness.
- 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_69a8891a19508190a12ef1e192308dcb |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9af42988190a4e977154dd10312 |
completed | March 7, 2026, 5:37 a.m. |
| PD | Predicate disambiguation | batch_69abb7ad5a7c8190b92575d6053b3fb7 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:40 p.m.