Triple
T1382709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss–Markov theorem |
E29373
|
entity |
| Predicate | typeOfOptimality |
P27210
|
FINISHED |
| Object | minimum variance |
—
|
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: minimum variance | Statement: [Gauss–Markov theorem, typeOfOptimality, minimum variance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfOptimality Context triple: [Gauss–Markov theorem, typeOfOptimality, minimum variance]
-
A.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
B.
rankFunctionCharacterization
Indicates that a function is being characterized or defined in terms of its rank, typically specifying how the rank property determines or describes the function’s behavior or classification.
-
C.
typeOfSignificance
Indicates that one entity specifies the nature or category of importance, relevance, or significance that another entity possesses.
-
D.
minimizedBy
Indicates that one entity serves to reduce, lessen, or make as small as possible the value, effect, or impact of another entity.
-
E.
selectionMetric
Indicates the criterion or measure used to evaluate and choose among alternative options or candidates.
- F. None of above. chosen
Provenance (4 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3361bf08190b3f6bbf82e17685b |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.