Triple
T1382710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss–Markov theorem |
E29373
|
entity |
| Predicate | typeOfEstimatorClass |
P27211
|
FINISHED |
| Object | linear estimators |
—
|
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: linear estimators | Statement: [Gauss–Markov theorem, typeOfEstimatorClass, linear estimators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfEstimatorClass Context triple: [Gauss–Markov theorem, typeOfEstimatorClass, linear estimators]
-
A.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
B.
typeOfEnsemble
Indicates the specific kind or category of ensemble that an entity belongs to or represents.
-
C.
kernelType
Indicates the specific kind or category of kernel associated with or used by an entity.
-
D.
typeOfCluster
Indicates that one entity is classified as a specific kind or category of cluster in relation to another entity.
-
E.
algorithmType
Indicates the specific kind or category of algorithm associated with an entity or process.
- 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.