Triple
T21197398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic of Vietnam Navy |
E522361
|
entity |
| Predicate | acronym |
P43
|
FINISHED |
| Object | RVNN |
—
|
NE NERFINISHED |
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: RVNN | Statement: [Republic of Vietnam Navy, acronym, RVNN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RVNN Context triple: [Republic of Vietnam Navy, acronym, RVNN]
-
A.
RVNN
chosen
RVNN is the acronym for the Republic of Vietnam Navy, the maritime military force of South Vietnam that operated primarily during the Vietnam War era.
-
B.
DNN
DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
-
C.
RUVNN
RUVNN is the international port code assigned to the seaport of Vanino in Russia.
-
D.
FNN
FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
-
E.
BNNS
BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7333c9bac8190a203802a8b8e4143 |
completed | April 21, 2026, 8:20 a.m. |
Created at: April 16, 2026, 3:11 p.m.