Triple
T16765713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ATLiens (song) |
E407456
|
entity |
| Predicate | nextSingle |
P25313
|
FINISHED |
| Object | Jazzy Belle |
E407458
|
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: Jazzy Belle | Statement: [ATLiens (song), nextSingle, Jazzy Belle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jazzy Belle Context triple: [ATLiens (song), nextSingle, Jazzy Belle]
-
A.
Jazzy Belle
chosen
"Jazzy Belle" is a soulful, introspective hip-hop track by OutKast that reflects on relationships, femininity, and personal growth.
-
B.
Belles
The Belles is the nickname for the athletic teams and student body of Bennett College, a historically Black liberal arts college for women in Greensboro, North Carolina.
-
C.
Belles
Belles is a surname of likely French origin borne by individuals such as Helen Artie Belles.
-
D.
Our Lady J
Our Lady J is an American writer, producer, and musician best known for her groundbreaking work on television series like "Transparent" and "Pose," where she has been a prominent trans creative voice.
-
E.
Nazz
Nazz is a popular, easygoing, and kind-hearted girl from the animated series "Ed, Edd n Eddy," often portrayed as the neighborhood crush.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abf126408190bd0365eca150f745 |
completed | April 18, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00aaf7908481909af31fc2d02f33fb |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 5:21 a.m.