Triple
T20609141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Time After Time |
E506397
|
entity |
| Predicate | precedesSingle |
P97
|
FINISHED |
| Object | She Bop |
—
|
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: She Bop | Statement: [Time After Time, precedesSingle, She Bop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: She Bop Context triple: [Time After Time, precedesSingle, She Bop]
-
A.
She Bop
chosen
"She Bop" is a 1984 pop song by Cyndi Lauper, known for its catchy new wave sound and its then-controversial, sex-positive lyrics that helped cement her image as a bold, unconventional pop icon.
-
B.
Boogie Lover
"Boogie Lover" is a song by the American rock band Black Mountain, known for its heavy, psychedelic sound.
-
C.
Diddy Bop
"Diddy Bop" is a laid-back, jazz-infused hip-hop track by Noname, known for its nostalgic lyrics and smooth, melodic production on her mixtape *Telefone*.
-
D.
Hip Hug-Her
"Hip Hug-Her" is a 1967 soul and R&B instrumental album by Booker T. & the M.G.'s, known for its tight grooves and influential Memphis sound.
-
E.
Go-Go Boots
Go-Go Boots is a 2011 studio album by American rock band Drive-By Truckers that blends Southern rock with soul and country influences.
- 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_69e0b4bb2b4081908fa4a72444120f35 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aad5e53c8190b0add34ce9b31d57 |
completed | April 20, 2026, 10:38 p.m. |
Created at: April 16, 2026, 11:41 a.m.