Triple
T16554598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dub Be Good to Me |
E402160
|
entity |
| Predicate | featuresVocalist |
P8086
|
FINISHED |
| Object | Lindy Layton |
E402160
|
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: Lindy Layton | Statement: [Dub Be Good to Me, featuresVocalist, Lindy Layton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lindy Layton Context triple: [Dub Be Good to Me, featuresVocalist, Lindy Layton]
-
A.
Lindy Layton
chosen
Lindy Layton is a British singer best known for her work in the early 1990s dance and electronic music scene, including the hit single "Dub Be Good to Me."
-
B.
Natalie Hemby
Natalie Hemby is an American singer-songwriter best known for her acclaimed country and Americana songwriting for artists like Miranda Lambert and for her work as a member of the supergroup The Highwomen.
-
C.
Lindsey Chapman
Lindsey Chapman is a British television and radio presenter best known for her work on nature and wildlife programmes.
-
D.
Annaleigh Ashford
Annaleigh Ashford is a Tony Award–winning American actress, singer, and dancer known for her work in Broadway musicals and television.
-
E.
Kat Barton
Kat Barton is a key supporting character who aids the main protagonist in their journey, often providing crucial assistance and companionship.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34fc887e881909607653df7fe71be |
completed | April 18, 2026, 9:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00758f97708190a289da0bd5d5c254 |
completed | May 10, 2026, 12:09 p.m. |
Created at: April 10, 2026, 5:15 a.m.