Triple
T19549966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruby |
E489170
|
entity |
| Predicate | relatedWorkBefore |
P136551
|
FINISHED |
| Object | Jazz |
—
|
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: Jazz | Statement: [Ruby, relatedWorkBefore, Jazz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jazz Context triple: [Ruby, relatedWorkBefore, Jazz]
-
A.
Jazz
chosen
"Jazz" is a 1992 novel by Toni Morrison that explores love, violence, and memory in 1920s Harlem through a narrative style inspired by the improvisational rhythms of jazz music.
-
B.
Jazz
Jazz is a celebrated illustrated book by Henri Matisse featuring vibrant color paper cut-outs that exemplify his late-career artistic style.
-
C.
Jazz
Jazz is a genre of music characterized by swing, improvisation, complex harmonies, and roots in African American musical traditions.
-
D.
Jazz
Jazz is a stylish, music-loving Autobot from the Transformers franchise, known for his cool demeanor, loyalty to Optimus Prime, and skill in combat and reconnaissance.
-
E.
Jazz
Jazz is a retired American professional wrestler best known for her powerful in-ring style and multiple reigns as WWE Women's Champion in the early 2000s.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63d2f39188190976e8b6b111499a0 |
completed | April 20, 2026, 2:50 p.m. |
Created at: April 10, 2026, 1:41 p.m.