Triple
T15924693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ministry of Internal Affairs of Serbia |
E386177
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
MUP
MUP is the commonly used abbreviation for Serbia’s Ministry of Internal Affairs, the government body responsible for police, security, and internal administrative affairs.
|
E1185302
|
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: MUP | Statement: [Ministry of Internal Affairs of Serbia, abbreviation, MUP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MUP Context triple: [Ministry of Internal Affairs of Serbia, abbreviation, MUP]
-
A.
MUF
MUF is the youth wing of Sweden's Moderate Party, engaging young people in center-right politics and policy issues.
-
B.
MUH
MUH is the IATA airport code for Marsa Matruh International Airport, which serves the coastal city of Mersa Matruh in Egypt.
-
C.
MPS
MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
-
D.
MPS
MPS (Metal Performance Shaders) is an Apple framework that provides highly optimized GPU-accelerated compute and graphics shaders for tasks like image processing and machine learning on Apple devices.
-
E.
MPS
MPS is the central government agency responsible for public security, policing, and domestic law enforcement in the People's Republic of China.
- 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: MUP Triple: [Ministry of Internal Affairs of Serbia, abbreviation, MUP]
Generated description
MUP is the commonly used abbreviation for Serbia’s Ministry of Internal Affairs, the government body responsible for police, security, and internal administrative affairs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MUP Target entity description: MUP is the commonly used abbreviation for Serbia’s Ministry of Internal Affairs, the government body responsible for police, security, and internal administrative affairs.
-
A.
MUF
MUF is the youth wing of Sweden's Moderate Party, engaging young people in center-right politics and policy issues.
-
B.
MUH
MUH is the IATA airport code for Marsa Matruh International Airport, which serves the coastal city of Mersa Matruh in Egypt.
-
C.
MPS
MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
-
D.
MPS
MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
-
E.
MPS
MPS (Metal Performance Shaders) is an Apple framework that provides highly optimized GPU-accelerated compute and graphics shaders for tasks like image processing and machine learning on Apple devices.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1568424f08190bffe6ee465a0db9a |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb5adcde88190ae2a845aaa9d31ac |
completed | May 9, 2026, 10:31 p.m. |
| NEDg | Description generation | batch_69ffb6c4a66c8190bba70da71c9ec576 |
completed | May 9, 2026, 10:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb7a373d88190a2fcf75022f3e161 |
completed | May 9, 2026, 10:39 p.m. |
Created at: April 10, 2026, 4:52 a.m.