Triple
T23170445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I'm a Fool to Want You |
E578842
|
entity |
| Predicate | lyricist |
P1360
|
FINISHED |
| Object | Jack Wolf |
—
|
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: Jack Wolf | Statement: [I'm a Fool to Want You, lyricist, Jack Wolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Wolf Context triple: [I'm a Fool to Want You, lyricist, Jack Wolf]
-
A.
Jack Wolf
chosen
Jack Wolf was an American songwriter best known for co-writing the jazz and pop standard "I'm a Fool to Want You."
-
B.
David Wolf
David Wolf is an American astronaut and physician known for his long-duration missions aboard the Russian Mir space station and the International Space Station.
-
C.
Richard Anthony Wolf
Richard Anthony Wolf is an American television producer and writer best known as the creator of the long-running "Law & Order" franchise.
-
D.
Berny Wolf
Berny Wolf was an American animator, director, and producer known for his long career at major studios like Disney and Hanna-Barbera, contributing to classic animated films and television series.
-
E.
C. Wolf
C. Wolf is the costumed wolf mascot who entertains fans at Erie SeaWolves minor league baseball games.
- 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_69e245fd2a388190b814c0dfa15f7148 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f2f10208190b3a0f9a4c790cb0a |
completed | April 29, 2026, 4:55 a.m. |
Created at: April 17, 2026, 4:03 p.m.