Triple
T16639448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R. City |
E404291
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Losing It
"Losing It" is a popular song by Canadian country duo R. City that showcases their blend of Caribbean-influenced pop and contemporary R&B.
|
E1224334
|
NE FINISHED |
How this triple was built (4 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: Losing It | Statement: [R. City, notableWork, Losing It]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Losing It Context triple: [R. City, notableWork, Losing It]
-
A.
Losing It All
"Losing It All" is a song by the American metalcore band Lowborn.
-
B.
Just Lose It
"Just Lose It" is a comedic hip hop single by Eminem, best known for its parody of Michael Jackson and its controversial music video.
-
C.
But We Lost It
"But We Lost It" is a song by American singer Pink, featured as a track on her 2017 album *Beautiful Trauma*.
-
D.
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.
-
E.
Nothing to Lose
Nothing to Lose is a thriller novel by Lee Child featuring his iconic drifter hero Jack Reacher as he investigates a sinister conspiracy in two neighboring Colorado towns.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Losing It Triple: [R. City, notableWork, Losing It]
Generated description
"Losing It" is a popular song by Canadian country duo R. City that showcases their blend of Caribbean-influenced pop and contemporary R&B.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Losing It Target entity description: "Losing It" is a popular song by Canadian country duo R. City that showcases their blend of Caribbean-influenced pop and contemporary R&B.
-
A.
Losing It All
"Losing It All" is a song by the American metalcore band Lowborn.
-
B.
Just Lose It
"Just Lose It" is a comedic hip hop single by Eminem, best known for its parody of Michael Jackson and its controversial music video.
-
C.
But We Lost It
"But We Lost It" is a song by American singer Pink, featured as a track on her 2017 album *Beautiful Trauma*.
-
D.
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.
-
E.
Nothing to Lose
Nothing to Lose is a thriller novel by Lee Child featuring his iconic drifter hero Jack Reacher as he investigates a sinister conspiracy in two neighboring Colorado towns.
- F. None of above. chosen
Provenance (5 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37acff38081908c8044936b794ce0 |
completed | April 18, 2026, 12:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007dc41638819090e967ade46d35a4 |
completed | May 10, 2026, 12:44 p.m. |
| NEDg | Description generation | batch_6a007e28aee48190873c76743aa1778e |
completed | May 10, 2026, 12:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a007f3bf6e081908554238d069d9abc |
completed | May 10, 2026, 12:51 p.m. |
Created at: April 10, 2026, 5:18 a.m.