Triple
T13059120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Views |
E329154
|
entity |
| Predicate | mainSingle |
P16320
|
FINISHED |
| Object | Too Good |
E1018135
|
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: Too Good | Statement: [Views, mainSingle, Too Good]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Too Good Context triple: [Views, mainSingle, Too Good]
-
A.
Too Good
chosen
Too Good is a song featured on Drake’s 2016 album "Views," known for its dancehall-influenced sound and collaboration with Rihanna.
-
B.
So Good
"So Good" is the 2017 debut international studio album by Swedish pop singer Zara Larsson, featuring hit singles like "Lush Life" and "Ain't My Fault."
-
C.
So Good
"So Good" is a popular hip hop and pop-rap single by American rapper B.o.B, known for its catchy melody and romantic, feel-good lyrics.
-
D.
So Good
"So Good" is a 2005 pop single by British singer Rachel Stevens, known for its sleek production and dance-pop sound.
-
E.
Be Good
"Be Good" is a critically acclaimed jazz and soul album by American singer-songwriter Gregory Porter, noted for its warm vocals, sophisticated songwriting, and emotional depth.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980be37208190962e91f1e19df159 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d6016984819097c999772db1e831 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 8:58 p.m.