Triple
T20337179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 10GBASE-R PCS |
E495643
|
entity |
| Predicate | lineCodeEfficiency |
P19896
|
FINISHED |
| Object | approximately 97 percent |
—
|
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: approximately 97 percent | Statement: [10GBASE-R PCS, lineCodeEfficiency, approximately 97 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lineCodeEfficiency Context triple: [10GBASE-R PCS, lineCodeEfficiency, approximately 97 percent]
-
A.
codeDensity
Indicates the proportion of code elements (such as instructions, statements, or functionality) relative to a given size or resource measure (e.g., lines, bytes, or area).
-
B.
usesLineCode
chosen
Indicates that one entity employs or references a specific line code as part of its operation, identification, or communication.
-
C.
sampleEfficiency
Indicates how effectively a method or system learns or performs using a limited number of samples or data points.
-
D.
netEfficiency
Indicates the overall effectiveness of a system or process after accounting for all losses, typically expressed as the ratio of useful output to total input.
-
E.
linesOfCodeApprox
Indicates an approximate or estimated number of lines of code associated with an entity.
- F. None of above.
Provenance (3 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677ed75e081909bfc534033ac1c59 |
completed | April 20, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69e5762655ac8190a8cc48a29fa2c0c4 |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:23 a.m.