Triple
T25602595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reed–Solomon codes |
E641825
|
entity |
| Predicate | erasureCorrectionCapabilityFormula |
P158900
|
FINISHED |
| Object | 2e + s < d where e errors and s erasures |
—
|
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: 2e + s < d where e errors and s erasures | Statement: [Reed–Solomon codes, erasureCorrectionCapabilityFormula, 2e + s < d where e errors and s erasures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: erasureCorrectionCapabilityFormula Context triple: [Reed–Solomon codes, erasureCorrectionCapabilityFormula, 2e + s < d where e errors and s erasures]
-
A.
usesForwardErrorCorrection
Indicates that one entity applies forward error correction techniques to detect and correct errors in data transmitted to or received from another entity.
-
B.
parityBits
Indicates that there is an association between data and the parity bits used to detect or correct errors in that data.
-
C.
errorDetectionCapability
Indicates the ability of an entity to detect the presence of errors in data, processes, or operations.
-
D.
blockCodeLengthFormula
Indicates a relationship where a formula specifies how to compute the length of a block of code.
-
E.
errorDetectionMethod
Indicates the method or technique used to detect errors in a process, system, or data.
- 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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f9a7d7d881909014fdf3746f981b |
completed | May 2, 2026, 1:18 p.m. |
| PD | Predicate disambiguation | batch_69f480789be08190ab252a6de3797200 |
completed | May 1, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 21, 2026, 4:36 p.m.