Triple
T8507285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hall of Fame |
E201364
|
entity |
| Predicate | hasSong |
P20452
|
FINISHED |
| Object | Guap |
E742877
|
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: Guap | Statement: [Hall of Fame, hasSong, Guap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guap Context triple: [Hall of Fame, hasSong, Guap]
-
A.
Guap
chosen
"Guap" is a hip-hop single by Big Sean, known for its catchy production and role in boosting his mainstream popularity.
-
B.
Yandel
Yandel is a Puerto Rican reggaeton singer and songwriter best known as one half of the duo Wisin & Yandel and for his influential solo work in Latin urban music.
-
C.
Farruko
Farruko is a Puerto Rican singer and songwriter known for his influential role in reggaeton and Latin trap music.
-
D.
Don Omar
Don Omar is a pioneering Puerto Rican reggaeton singer, songwriter, and producer widely regarded as one of the genre’s most influential and commercially successful artists.
-
E.
J Balvin
J Balvin is a Colombian reggaeton singer and global Latin music star known for hits like "Mi Gente" and high-profile collaborations across pop and hip-hop.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5de18448190a695eec609b34e1a |
completed | March 31, 2026, 3:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea8411e148190a700dae3d3ebd716 |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:14 p.m.