Triple
T22184939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Règle du jeu |
E548269
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Joseph Kosma |
—
|
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: Joseph Kosma | Statement: [La Règle du jeu, musicBy, Joseph Kosma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Kosma Context triple: [La Règle du jeu, musicBy, Joseph Kosma]
-
A.
Joseph Kosma
chosen
Joseph Kosma was a Hungarian-French composer best known for his film scores and popular songs, including the classic "Autumn Leaves."
-
B.
Anton Karas
Anton Karas was an Austrian zither player and composer best known for his iconic zither score for the film "The Third Man."
-
C.
Karl Straube
Karl Straube was a prominent German organist, choral conductor, and influential interpreter of Bach and Reger who served as Thomaskantor in Leipzig.
-
D.
Alfred Kralik
Alfred Kralik is the earnest and principled sales clerk who serves as the male lead in the classic romantic film "The Shop Around the Corner."
-
E.
George Kozmetsky
George Kozmetsky was an American technology entrepreneur, investor, and educator best known as a co-founder of Teledyne and a major figure in fostering innovation and high-tech industry growth.
- 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aa823888190829368de6db4aa91 |
completed | April 28, 2026, 9:46 p.m. |
Created at: April 16, 2026, 8:35 p.m.