Triple
T21763587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oh Mercy |
E537222
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | What Good Am I? |
—
|
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: What Good Am I? | Statement: [Oh Mercy, hasPart, What Good Am I?]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: What Good Am I? Context triple: [Oh Mercy, hasPart, What Good Am I?]
-
A.
What Good Am I?
chosen
"What Good Am I?" is a reflective, introspective song by Bob Dylan from his late-1980s period, noted for its themes of moral self-doubt and emotional responsibility.
-
B.
The Good in Me
"The Good in Me" is a song featured on the 2012 album *The Human Condition* by American rapper and producer Jon Bellion.
-
C.
Is It Good to You
"Is It Good to You" is an early-1990s hip hop and R&B track by Heavy D & the Boyz known for its smooth groove and flirtatious lyrics.
-
D.
Good To Me
"Good To Me" is an R&B song by American singer LeToya Luckett, known for showcasing her smooth vocals and emotive, relationship-focused lyrics.
-
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 (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_69e0c46f5d1c8190bf830409e98464e5 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f031a711dc8190a786c9849dc344e8 |
completed | April 28, 2026, 4:03 a.m. |
Created at: April 16, 2026, 6:51 p.m.