Triple
T4461566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wigner surmise |
E98265
|
entity |
| Predicate | exactFor |
P55562
|
FINISHED |
| Object | 2×2 random matrices |
—
|
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: 2×2 random matrices | Statement: [Wigner surmise, exactFor, 2×2 random matrices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exactFor Context triple: [Wigner surmise, exactFor, 2×2 random matrices]
-
A.
exactValueReason
Indicates that the value is specified exactly as given due to a particular justification or rationale.
-
B.
providedFor
Indicates that one entity supplies, furnishes, or makes something available to or on behalf of another entity for its use or benefit.
-
C.
calibratedFor
Indicates that something has been adjusted or tuned to operate accurately or optimally for a specific target, condition, or context.
-
D.
givenFor
Indicates that something is provided, offered, or assigned in favor of or on behalf of a particular entity or purpose.
-
E.
precision
Indicates the degree to which an action, measurement, or outcome is carried out with exactness, minimal deviation, and fine-grained accuracy.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35674f718819089388c3924dd1414 |
completed | March 13, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69b34f65f6448190abfadb2ae5658798 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff7018c81908ad8597e525c042b |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:34 p.m.