Triple
T10572415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vixen |
E249519
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | “How Much Love” |
E873361
|
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: “How Much Love” | Statement: [Vixen, notableSong, “How Much Love”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “How Much Love” Context triple: [Vixen, notableSong, “How Much Love”]
-
A.
“How Much Love”
chosen
“How Much Love” is a hard rock song by the American glam metal band Vixen, known from their late-1980s/early-1990s catalog.
-
B.
"Does He Love You"
"Does He Love You" is a hit country duet, originally recorded by Reba McEntire and Linda Davis, known for its dramatic narrative about a love triangle and emotional vocal performances.
-
C.
“I’m Gonna Love You”
“I’m Gonna Love You” is an R&B song written and produced by Shep Crawford, known for his emotionally driven, soulful ballads.
-
D.
"Sweet Love"
"Sweet Love" is a smooth, soulful R&B ballad by Anita Baker that became one of her signature hits in the mid-1980s.
-
E.
Lots of Lovin'
"Lots of Lovin'" is a soulful, jazz-infused hip-hop track by CL Smooth (with producer Pete Rock) known for its smooth delivery and introspective lyrics.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d52747a8d88190ab59333937180edc |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e7396a4819082cc73c736636fb9 |
completed | April 10, 2026, 8:32 p.m. |
Created at: April 6, 2026, 12:37 p.m.