Triple
T12410588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African Giant |
E296502
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | On the Low (song) |
E294489
|
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: On the Low (song) | Statement: [African Giant, hasPart, On the Low (song)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: On the Low (song) Context triple: [African Giant, hasPart, On the Low (song)]
-
A.
On the Low
chosen
"On the Low" is a popular Afro-fusion song by Nigerian artist Burna Boy, known for its smooth melody, romantic lyrics, and widespread international success.
-
B.
So Low
"So Low" is a song by American rapper Talib Kweli from his album *Gutter Rainbows*, showcasing his socially conscious lyricism over soulful, boom-bap-influenced production.
-
C.
Down So Low
"Down So Low" is a soulful ballad best known from Linda Ronstadt’s 1976 album *Hasten Down the Wind*, originally written and recorded by singer-songwriter Tracy Nelson.
-
D.
How Low
"How Low" is a popular hip-hop single by American rapper Ludacris, known for its catchy hook and heavy club-oriented production.
-
E.
Bend Down Low
"Bend Down Low" is a reggae song by Bob Marley featured on his influential 1974 album "Natty Dread."
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d4b86c88190afba0de15b34eee9 |
completed | April 10, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63efe60388190944fe3226be4cc7c |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:55 p.m.