Triple
T18030979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Lady |
E431387
|
entity |
| Predicate | album |
P1995
|
FINISHED |
| Object | Tyrese |
—
|
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: Tyrese | Statement: [Sweet Lady, album, Tyrese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyrese Context triple: [Sweet Lady, album, Tyrese]
-
A.
Tyrese Gibson
chosen
Tyrese Gibson is an American R&B singer and actor best known for his music career and prominent roles in the Fast & Furious and Transformers film franchises.
-
B.
Tyga
Tyga is an American rapper and songwriter known for hits like "Rack City" and his association with the Young Money Entertainment label.
-
C.
Trey Songz
Trey Songz is an American R&B singer, songwriter, and occasional actor known for his smooth vocals and sensual, contemporary R&B hits.
-
D.
Lyriq Bent
Lyriq Bent is a Canadian actor known for his roles in the Saw film series, the television miniseries Book of Negroes, and various film and TV dramas.
-
E.
Obie Trice
Obie Trice is an American rapper from Detroit known for his work with Shady Records and his appearance on the soundtrack of the film "8 Mile."
- 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_69d8b9050fb48190890155145deb0a66 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4be35a70081909d24d36506d18131 |
completed | April 19, 2026, 11:36 a.m. |
Created at: April 10, 2026, 10:25 a.m.