Triple
T31776281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SLC (single-level cell) |
E811072
|
entity |
| Predicate | hasLatency |
P131053
|
FINISHED |
| Object | low latency |
—
|
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: low latency | Statement: [SLC (single-level cell), hasLatency, low latency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLatency Context triple: [SLC (single-level cell), hasLatency, low latency]
-
A.
communicationLatency
Indicates the time delay between when a communication is sent by one entity and when it is received or processed by another.
-
B.
latencyLowerThan
Indicates that the latency of one entity is strictly less than the latency of another entity.
-
C.
latencyClass
chosen
Indicates the category or range of delay (latency) associated with an interaction, communication, or response between entities.
-
D.
latencyModel
Indicates a relationship where one entity serves as or uses a specific model to represent, estimate, or analyze latency characteristics of another entity or process.
-
E.
latencySite
Indicates a relationship where a specific site or location is associated with the latency experienced in a process, transmission, or interaction.
- 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_69f348e544a48190ab6e700b05f6438c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe6e492bf8819080b25221d13445ea |
completed | May 8, 2026, 11:14 p.m. |
| PD | Predicate disambiguation | batch_69fe6dd33a6881908fe9bbbc184cab51 |
completed | May 8, 2026, 11:12 p.m. |
Created at: April 30, 2026, 11:35 p.m.