Triple
T22704839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 99 Problems |
E561421
|
entity |
| Predicate | songwriter |
P1141
|
FINISHED |
| Object | Norman Landsberg |
—
|
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: Norman Landsberg | Statement: [99 Problems, songwriter, Norman Landsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norman Landsberg Context triple: [99 Problems, songwriter, Norman Landsberg]
-
A.
Norman Landsberg
chosen
Norman Landsberg is a songwriter best known for co-writing the track "99 Problems."
-
B.
Morton Schmidt
Morton Schmidt is a socially awkward but well-meaning rookie cop, portrayed by Jonah Hill, who goes undercover as a high school student in the action-comedy film "21 Jump Street."
-
C.
Norman Fruchter
Norman Fruchter was an American writer, filmmaker, and education reform advocate known for his work on urban schooling and community-based education.
-
D.
Norman Wray
Norman Wray is an Ecuadorian politician known for his candidacy in national elections and involvement in progressive political movements.
-
E.
Walter Leistikow
Walter Leistikow was a German painter and graphic artist associated with German Impressionism and a leading figure in Berlin’s modern art movement around 1900.
- 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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178cdc93481908f85d04560f8c285 |
completed | April 29, 2026, 3:19 a.m. |
Created at: April 17, 2026, 3:16 p.m.