Triple
T72802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SCSI |
E1457
|
entity |
| Predicate | maxDevicesPerBus |
P4619
|
FINISHED |
| Object | 8 (narrow SCSI including host) |
—
|
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: 8 (narrow SCSI including host) | Statement: [SCSI, maxDevicesPerBus, 8 (narrow SCSI including host)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxDevicesPerBus Context triple: [SCSI, maxDevicesPerBus, 8 (narrow SCSI including host)]
-
A.
maxHostsPerIMP
Indicates the maximum number of hosts that can be associated with or managed by a single IMP (Interface Message Processor).
-
B.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
C.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
D.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
-
E.
maximumService
Indicates that an entity provides the highest allowable or achievable level of service within a given context or 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a252201fa481908e30791954119c17 |
completed | Feb. 28, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69a24eacfdc481909e9ff99752fd42bf |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2521ed5088190bbdfd22164bb4d94 |
completed | Feb. 28, 2026, 2:25 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.