Triple
T7422163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minneapolis Institute of Art |
E171275
|
entity |
| Predicate | hasShortName |
P1354
|
FINISHED |
| Object | MIA |
E171275
|
NE FINISHED |
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: MIA | Statement: [Minneapolis Institute of Art, hasShortName, MIA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MIA Context triple: [Minneapolis Institute of Art, hasShortName, MIA]
-
A.
MIA
MIA is the standard three-letter abbreviation used to represent the Miami Marlins Major League Baseball team.
-
B.
MIA
MIA is the UN/LOCODE designation for Miami, a major coastal city and transportation hub in the U.S. state of Florida.
-
C.
MIA
chosen
MIA is a major fine arts museum in Minneapolis, Minnesota, known for its extensive global art collections spanning thousands of years.
-
D.
M.I.A.
M.I.A. is a British-Sri Lankan rapper, singer, and visual artist known for her politically charged lyrics and genre-blending hits like "Paper Planes."
-
E.
Messy Mya
Messy Mya was a New Orleans bounce rapper, comedian, and internet personality known for his viral YouTube videos and influence on the city’s bounce music scene.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2ed29ec8190804564185fe20797 |
completed | March 27, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81effc488819086336eea92604fa8 |
completed | March 28, 2026, 6:33 p.m. |
Created at: March 27, 2026, 3:11 p.m.