Triple
T5416972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fly Me to the Moon |
E121155
|
entity |
| Predicate | lyricist |
P1360
|
FINISHED |
| Object | Bart Howard |
E518950
|
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: Bart Howard | Statement: [Fly Me to the Moon, lyricist, Bart Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bart Howard Context triple: [Fly Me to the Moon, lyricist, Bart Howard]
-
A.
Bart Howard
chosen
Bart Howard was an American songwriter best known for writing the jazz standard "Fly Me to the Moon."
-
B.
Steve Hawley
Steve Hawley is a former NASA astronaut and astronomer who flew on five Space Shuttle missions.
-
C.
Barry Kelley
Barry Kelley was an American character actor known for his tough, authoritative roles in mid-20th-century film noir and crime dramas.
-
D.
Glenn Holland
Glenn Holland is the dedicated high school music teacher and composer at the heart of the film "Mr. Holland's Opus," whose life’s work becomes the impact he has on his students.
-
E.
Ron Torbert
Ron Torbert is an American NFL official who has served as a referee in multiple high-profile games, including Super Bowl LVI.
- 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87bff75881909bdfd2cdf7ff5657 |
completed | March 20, 2026, 5:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf4121edc4819081fdb79dcc182540 |
completed | March 22, 2026, 1:08 a.m. |
Created at: March 20, 2026, 2:05 p.m.