Triple

T5490991
Position Surface form Disambiguated ID Type / Status
Subject Republic of Vietnam Navy E123699 entity
Predicate alsoKnownAs P39 FINISHED
Object RVNN
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.
 E522361 NE FINISHED

How this triple was built (4 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, alsoKnownAs, RVNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RVNN
Context triple: [Republic of Vietnam Navy, alsoKnownAs, RVNN]
  • A. RUVNN
    RUVNN is the international port code assigned to the seaport of Vanino in Russia.
  • B. VGG
    VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
  • C. VRN
    VRN is the public transport association serving Germany’s Rhine-Neckar metropolitan region, coordinating regional and local transit services across multiple states.
  • D. RBM
    RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
  • E. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: RVNN
Triple: [Republic of Vietnam Navy, alsoKnownAs, RVNN]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RVNN
Target entity description: 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.
  • A. RUVNN
    RUVNN is the international port code assigned to the seaport of Vanino in Russia.
  • B. VGG
    VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
  • C. VRN
    VRN is the public transport association serving Germany’s Rhine-Neckar metropolitan region, coordinating regional and local transit services across multiple states.
  • D. RBM
    RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
  • E. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • F. None of above. chosen

Provenance (5 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_69bd464a2d908190869324ce176779c8 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd927f198c8190889b555be9bf9765 completed March 20, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf48ada0548190aea4c6bd9013118f completed March 22, 2026, 1:41 a.m.
NEDg Description generation batch_69bf498739e8819093a399c4330b62cf completed March 22, 2026, 1:44 a.m.
NED2 Entity disambiguation (via description) batch_69bf49eede0081909843a7984aadf4ad completed March 22, 2026, 1:46 a.m.
Created at: March 20, 2026, 2:10 p.m.