Triple
T21455321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Got Next |
E529323
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Mad Lion |
—
|
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: Mad Lion | Statement: [I Got Next, featuresArtist, Mad Lion]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mad Lion Context triple: [I Got Next, featuresArtist, Mad Lion]
-
A.
Mad Lion
chosen
Mad Lion is a Jamaican-born ragga and hip-hop artist known for blending dancehall with East Coast rap in the 1990s.
-
B.
Roaring Lion
Roaring Lion was a top-class British Thoroughbred racehorse best known for his outstanding three-year-old season in 2018, during which he won multiple Group 1 races and was named Cartier Horse of the Year.
-
C.
Roaring Lion
Roaring Lion is the costumed lion mascot that represents Finlandia University at its athletic events and school functions.
-
D.
Lion-Mher
Lion-Mher is a heroic figure from the Armenian epic cycle "Daredevils of Sassoun," renowned for his extraordinary strength and lion-like bravery.
-
E.
Old Tige
Old Tige was the nickname of William L. Cabell, a Confederate general who later became a prominent postwar civic leader and mayor of Dallas, Texas.
- 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_69e0c458133481908ae8b41a12c4edec |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d612e081909d00ca59a3621cc9 |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:08 p.m.