Triple
T15698347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Too Many Parents |
E380523
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | John Leipold |
—
|
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: John Leipold | Statement: [Too Many Parents, musicBy, John Leipold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Leipold Context triple: [Too Many Parents, musicBy, John Leipold]
-
A.
John Leipold
chosen
John Leipold was an American film composer best known for his work scoring early Hollywood films, particularly during the 1930s.
-
B.
Charles Weinstock
Charles Weinstock is a film producer best known for his work on the crime thriller movie "Fracture."
-
C.
Leonard Hirschfield
Leonard Hirschfield was a cinematographer best known for his work on the 1962 psychological drama film "David and Lisa."
-
D.
William Scharf
William Scharf is a film editor known for his work on feature films such as the comedy "Down Periscope."
-
E.
Wilm Hosenfeld
Wilm Hosenfeld was a German Wehrmacht officer known for secretly helping and ultimately saving Polish Jews, most famously the pianist Władysław Szpilman, during World War II.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f6c3328819081325b702ca92862 |
completed | April 16, 2026, 2:54 a.m. |
Created at: April 10, 2026, 4:44 a.m.