Triple
T18962518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One by One |
E463946
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Have It All |
—
|
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: Have It All | Statement: [One by One, hasTrack, Have It All]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Have It All Context triple: [One by One, hasTrack, Have It All]
-
A.
Have It All
chosen
"Have It All" is a feel-good pop song by Jason Mraz that delivers an uplifting, optimistic message about gratitude, possibility, and wishing others well.
-
B.
We Had It All
"We Had It All" is a country song best known through Waylon Jennings’ influential 1973 recording, which helped cement its status as a genre standard covered by numerous artists.
-
C.
If I Had It All
"If I Had It All" is a reflective rock song by the Dave Matthews Band, featured on their 2001 album "Everyday."
-
D.
We Could Have It All
"We Could Have It All" is a song by P!nk from her 2019 studio album "Hurts 2B Human."
-
E.
She’s Got It All
"She’s Got It All" is a 1997 country love song by Kenny Chesney that became one of his early chart-topping hits.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d368488190b0c7489335e5dd91 |
completed | April 20, 2026, 7:29 a.m. |
Created at: April 10, 2026, noon