Triple
T15877943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Koopa |
E384997
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | King of the Koopas |
E367856
|
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: King of the Koopas | Statement: [King Koopa, title, King of the Koopas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: King of the Koopas Context triple: [King Koopa, title, King of the Koopas]
-
A.
King of the Koopas
chosen
King of the Koopas is the fearsome turtle-like monarch who serves as Mario’s primary nemesis in Nintendo’s Super Mario video game series.
-
B.
Kiddy Kong
Kiddy Kong is a young, strong baby ape character from the Donkey Kong video game series, known for starring alongside Dixie Kong in Donkey Kong Country 3.
-
C.
World Bowser
World Bowser is the final, Bowser-themed amusement-park-style world and endgame area in Super Mario 3D World.
-
D.
Neo Bowser City
Neo Bowser City is a rain-soaked, neon-lit Bowser-themed race track set high above a futuristic cityscape in the Mario Kart series.
-
E.
Mario Banana No. 2
Mario Banana No. 2 is an underground 1960s avant-garde film associated with the New York queer art scene, featuring drag performer Mario Montez in a campy, experimental style.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fec9d4819081efea504e1e3952 |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa950a890819092bc1e8895034593 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:51 a.m.