Triple
T22126996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Take Me Home |
E546814
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Rami Yacoub |
—
|
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: Rami Yacoub | Statement: [Take Me Home, producer, Rami Yacoub]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rami Yacoub Context triple: [Take Me Home, producer, Rami Yacoub]
-
A.
Rami Yacoub
chosen
Rami Yacoub is a Swedish music producer and songwriter known for crafting numerous international pop hits with artists such as Celine Dion, Britney Spears, and the Backstreet Boys.
-
B.
Matthew Ansara
Matthew Ansara was the son of actors Michael Ansara and Barbara Eden, known for his brief acting career and his tragic death at a young age.
-
C.
Mena Massoud
Mena Massoud is an Egyptian-Canadian actor best known for starring as Aladdin in Disney’s 2019 live-action adaptation of the animated classic.
-
D.
Rami Afuni
Rami Afuni is a music producer and songwriter known for his work on R&B and pop tracks, including contributions to Toni Braxton’s hit “You’re Makin’ Me High.”
-
E.
Rami Nassar
Rami Nassar is a technology and innovation leader known for his work in digital strategy, product development, and emerging technologies.
- 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_69e11e39bf348190b541bfa16a7b71e0 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12982eaa08190933d3036c020f562 |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.