Triple
T26253870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | extended binary Golay code |
E656668
|
entity |
| Predicate | errorCorrectionCapability |
P160544
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [extended binary Golay code, errorCorrectionCapability, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: errorCorrectionCapability Context triple: [extended binary Golay code, errorCorrectionCapability, 3]
-
A.
errorCorrectionCapabilitySymbol
Indicates the symbol or notation used to represent an entity’s error correction capability in a given context or system.
-
B.
errorCorrectionCapabilityFormula
Indicates the mathematical expression that defines how much error correction a system or code can provide under given conditions.
-
C.
erasureCorrectionCapabilityFormula
Indicates the capability or method by which erasures can be corrected, typically expressed as a formal rule or formula describing that correction process.
-
D.
usesForwardErrorCorrection
Indicates that one entity applies forward error correction techniques to detect and correct errors in data transmitted to or received from another entity.
-
E.
errorDetectionCapability
Indicates the ability of an entity to detect the presence of errors in data, processes, or operations.
- 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_69ee5b4d25ac819086acb51184602576 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f60dcc29cc81908880bd825cb12141 |
completed | May 2, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69f602d2ec748190ae95154f34c7878f |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f6037bf7a081908862a8359be80cf8 |
completed | May 2, 2026, 2 p.m. |
Created at: April 26, 2026, 9:08 p.m.