Triple
T5160138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludaversal |
E116414
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Usher |
E64985
|
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: Usher | Statement: [Ludaversal, featuresArtist, Usher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Usher Context triple: [Ludaversal, featuresArtist, Usher]
-
A.
Usher
chosen
Usher is an American R&B singer, songwriter, dancer, and actor known for his smooth vocals, dynamic performances, and multi-platinum albums such as "Confessions."
-
B.
Usher II
Usher II is a short story by Ray Bradbury, set on Mars, that satirizes censorship and pays homage to Edgar Allan Poe within the larger collection The Martian Chronicles.
-
C.
Ne-Yo
Ne-Yo is an American R&B singer, songwriter, and record producer known for hits like "So Sick" and for writing songs for numerous major artists.
-
D.
Chris Brown
Chris Brown is a film producer known for his work on the World War II drama "The Railway Man."
-
E.
Chris Brown
Chris Brown is an American singer, songwriter, dancer, and actor known for his R&B and pop hits as well as his dynamic performances.
- 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd790613bc819084765cd4ea648dc9 |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed01ce48081908813348b762bf1b0 |
completed | March 21, 2026, 5:06 p.m. |
Created at: March 20, 2026, 1:44 p.m.