Triple
T5095109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Morning, Vietnam |
E114846
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Alex North |
E192506
|
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: Alex North | Statement: [Good Morning, Vietnam, musicBy, Alex North]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alex North Context triple: [Good Morning, Vietnam, musicBy, Alex North]
-
A.
Alex North
chosen
Alex North was an American composer renowned for his innovative and influential film scores, including his work on major Hollywood epics and dramas.
-
B.
David Raksin
David Raksin was an American film composer best known for his influential scores in classic Hollywood cinema, including the iconic music for the film "Laura."
-
C.
Bernard Newman
Bernard Newman was an American costume designer best known for his glamorous work in 1930s Hollywood musicals and films.
-
D.
Victor Young
Victor Young was an American composer, arranger, and conductor best known for his prolific film scores and popular songs during Hollywood’s Golden Age.
-
E.
Elmer Bernstein
Elmer Bernstein was an American composer renowned for his prolific and influential film scores across genres, including classics like "The Ten Commandments," "The Magnificent Seven," and "To Kill a Mockingbird."
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7563ad608190879a26a0bf07c3f6 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba7b87c08190a2581c87f965fa9f |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:40 p.m.