Triple
T7442915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic of Singapore Navy |
E171798
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
RSN
RSN is the maritime branch of Singapore’s armed forces, responsible for safeguarding the nation’s territorial waters and maritime interests.
|
E665532
|
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: RSN | Statement: [Republic of Singapore Navy, abbreviation, RSN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RSN Context triple: [Republic of Singapore Navy, abbreviation, RSN]
-
A.
RSN
RSN is the common abbreviation for Red Sox Nation, the passionate fan base of Major League Baseball’s Boston Red Sox.
-
B.
SRNS
SRNS is the management and operations contractor responsible for running the U.S. Department of Energy’s Savannah River Site, a key nuclear materials and environmental cleanup facility in South Carolina.
-
C.
RJSN
RJSN is the ICAO airport code for Niigata Airport in Niigata, Japan.
-
D.
R^nRS
R^nRS is the conventional notation for the successive Revised Reports on the Algorithmic Language Scheme, denoting the nth formal revision of the Scheme language specification.
-
E.
RSL
RSL is the shading language used in Pixar's RenderMan system to define the appearance of surfaces, lights, and volumes in high-end computer graphics rendering.
- 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: RSN Triple: [Republic of Singapore Navy, abbreviation, RSN]
Generated description
RSN is the maritime branch of Singapore’s armed forces, responsible for safeguarding the nation’s territorial waters and maritime interests.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RSN Target entity description: RSN is the maritime branch of Singapore’s armed forces, responsible for safeguarding the nation’s territorial waters and maritime interests.
-
A.
RSN
RSN is the common abbreviation for Red Sox Nation, the passionate fan base of Major League Baseball’s Boston Red Sox.
-
B.
SRNS
SRNS is the management and operations contractor responsible for running the U.S. Department of Energy’s Savannah River Site, a key nuclear materials and environmental cleanup facility in South Carolina.
-
C.
RJSN
RJSN is the ICAO airport code for Niigata Airport in Niigata, Japan.
-
D.
R^nRS
R^nRS is the conventional notation for the successive Revised Reports on the Algorithmic Language Scheme, denoting the nth formal revision of the Scheme language specification.
-
E.
RSL
RSL is the shading language used in Pixar's RenderMan system to define the appearance of surfaces, lights, and volumes in high-end computer graphics rendering.
- 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_69c68a65402881908f7869368eb746fb |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f36d0fbc81908cb7cfe99f80de08 |
completed | March 27, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8279c9cec8190bde450f845f7d0ea |
completed | March 28, 2026, 7:10 p.m. |
| NEDg | Description generation | batch_69c828c8b0588190a5a99380dc25d837 |
completed | March 28, 2026, 7:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8296962b48190b9f5cc4a66b93b91 |
completed | March 28, 2026, 7:18 p.m. |
Created at: March 27, 2026, 3:13 p.m.