Triple
T6253831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sacha Skarbek |
E140110
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Too Lost In You |
E580141
|
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: Too Lost In You | Statement: [Sacha Skarbek, notableWork, Too Lost In You]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Too Lost In You Context triple: [Sacha Skarbek, notableWork, Too Lost In You]
-
A.
Too Lost In You
chosen
"Too Lost in You" is a pop ballad by the Sugababes, known for its emotional lyrics and inclusion on the "Love Actually" film soundtrack.
-
B.
You Lost Me
"You Lost Me" is a soulful pop ballad by Christina Aguilera from her 2010 album *Bionic*, noted for its emotional vocals and themes of heartbreak and betrayal.
-
C.
Losing You
"Losing You" is an indie pop and R&B-influenced song by Solange Knowles, acclaimed for its bittersweet lyrics, minimalist production, and stylish, retro-inspired music video.
-
D.
Unless It's with You
"Unless It's with You" is a pop ballad by Christina Aguilera from her 2018 album "Liberation."
-
E.
Let’s Get Lost
"Let’s Get Lost" is a 1988 documentary film that chronicles the turbulent life and career of jazz trumpeter and singer Chet Baker.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063625608819081f5422112c80ce5 |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c519246a588190b14ad9331e1a5eea |
completed | March 26, 2026, 11:31 a.m. |
Created at: March 22, 2026, 4:24 p.m.