Triple
T12890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IEEE 1394 |
E259
|
entity |
| Predicate | dataRate |
P1376
|
FINISHED |
| Object | up to 400 Mbit/s in IEEE 1394-1995 |
—
|
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: up to 400 Mbit/s in IEEE 1394-1995 | Statement: [IEEE 1394, dataRate, up to 400 Mbit/s in IEEE 1394-1995]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataRate Context triple: [IEEE 1394, dataRate, up to 400 Mbit/s in IEEE 1394-1995]
-
A.
dataUse
Indicates how data is intended to be accessed, processed, or applied within a particular context or activity.
-
B.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
C.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
D.
frequency
Indicates how often an event, action, or relationship occurs within a given period or context.
-
E.
trackGauge
Indicates the distance between the inner faces of the rails in a railway track system.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a243abb2ec8190937365e5ecec52ad |
completed | Feb. 28, 2026, 1:23 a.m. |
| PD | Predicate disambiguation | batch_69a23fe9470c8190918a6ca1df168646 |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a243aa85848190813154e8a6495200 |
completed | Feb. 28, 2026, 1:23 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.