Triple
T2882473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Major and the Minor |
E59427
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object | Leo Tover |
E82298
|
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: Leo Tover | Statement: [The Major and the Minor, cinematography, Leo Tover]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leo Tover Context triple: [The Major and the Minor, cinematography, Leo Tover]
-
A.
Leo Tover
chosen
Leo Tover was an American cinematographer known for his work on numerous classic Hollywood films across several decades.
-
B.
Leon Feldhendler
Leon Feldhendler was a Polish Jewish resistance leader and Holocaust survivor best known for co-organizing the 1943 prisoner uprising at the Sobibor extermination camp.
-
C.
Frank Basile
Frank Basile is an American jazz baritone saxophonist and bandleader known for his work in the New York jazz scene.
-
D.
Richard Leibler
Richard Leibler was an American mathematician and statistician best known for co-developing the Kullback–Leibler divergence, a fundamental concept in information theory and statistics.
-
E.
Charles Lachman
Charles Lachman was a businessman and entrepreneur best known as a co-founder of the American cosmetics company Revlon.
- 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe02aa5948190a2e0bd9168232bd5 |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03167d7dc819093e91e0d42f3de6f |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:03 p.m.