Triple
T20567571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Majelis Permusyawaratan Rakyat |
E505002
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | MPR RI |
—
|
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: MPR RI | Statement: [Majelis Permusyawaratan Rakyat, abbreviation, MPR RI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MPR RI Context triple: [Majelis Permusyawaratan Rakyat, abbreviation, MPR RI]
-
A.
MPR
chosen
MPR is the Indonesian acronym for the People's Consultative Assembly, the country's highest constitutional body responsible for key legislative and constitutional functions.
-
B.
MMPR
MMPR is the ICAO airport code for Licenciado Gustavo Díaz Ordaz International Airport serving Puerto Vallarta, Mexico.
-
C.
MPRT
MPRT is the Swedish Press and Broadcasting Authority responsible for supervising and regulating press, radio, and television in Sweden.
-
D.
MPS
MPS is the central government agency responsible for public security, policing, and domestic law enforcement in the People's Republic of China.
-
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.
- 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a7a33f7c8190966da03528dfe8aa |
completed | April 20, 2026, 10:24 p.m. |
Created at: April 16, 2026, 11:39 a.m.