Triple
T5627240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dear Mama |
E147747
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Johnny J |
E535097
|
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: Johnny J | Statement: [Dear Mama, producer, Johnny J]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johnny J Context triple: [Dear Mama, producer, Johnny J]
-
A.
Johnny "J"
chosen
Johnny "J" was a prominent hip-hop producer best known for his extensive work with Tupac Shakur, crafting many of the rapper’s signature West Coast tracks.
-
B.
Johnny U
Johnny U is the legendary Hall of Fame NFL quarterback Johnny Unitas, renowned for revolutionizing the modern passing game with the Baltimore Colts.
-
C.
Johnny
Johnny is a common English masculine given name, often used as a familiar or diminutive form of John.
-
D.
John-John
John-John is the childhood nickname of John F. Kennedy Jr., the son of U.S. President John F. Kennedy and First Lady Jacqueline Kennedy Onassis.
-
E.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
- 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_69c00906f2a88190a992c66b13d606d4 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02237dd6081909f6a7d710b9cd651 |
completed | March 22, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a12b5bc8190a300a53a6423e81c |
completed | March 22, 2026, 9:07 p.m. |
Created at: March 22, 2026, 3:40 p.m.