Triple
T12463444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edge Virtual Bridging |
E297858
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | EVB |
E297859
|
NE 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: EVB | Statement: [Edge Virtual Bridging, alsoKnownAs, EVB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EVB Context triple: [Edge Virtual Bridging, alsoKnownAs, EVB]
-
A.
EVB
chosen
EVB (Edge Virtual Bridging) is an IEEE networking standard that defines mechanisms for managing and integrating virtualized network interfaces on edge switches and servers in data center environments.
-
B.
EVS
EVS (Enhanced Voice Services) is a modern audio codec designed to deliver high-quality, low-latency voice and audio over LTE and other IP-based communication networks.
-
C.
AEVB
AEVB (Auto-Encoding Variational Bayes) is a foundational variational inference framework that combines neural networks and probabilistic modeling to learn latent representations of data, most notably underpinning variational autoencoders (VAEs).
-
D.
Evo
Evo is the commonly used first name of Evo Morales, the former president of Bolivia and a prominent leftist political leader in Latin America.
-
E.
EBE
EBE is the vehicle registration code used for cars registered in the Ebersberg district of Bavaria, Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94db5efe88190a76949e4ddc3314c |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f1f44f481909c7efdffd2aeac41 |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:56 p.m.