Triple
T19872861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let It Go |
E477562
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Herb Magidson |
—
|
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: Herb Magidson | Statement: [Let It Go, writer, Herb Magidson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herb Magidson Context triple: [Let It Go, writer, Herb Magidson]
-
A.
Herb Magidson
chosen
Herb Magidson was an American lyricist and songwriter known for his popular songs of the 1930s and 1940s, including several Academy Award–winning works.
-
B.
Mack Emerman
Mack Emerman was an American recording engineer and producer best known as the founder of Miami’s influential Criteria Studios, where numerous landmark albums and hits were recorded.
-
C.
Walt Dohrn
Walt Dohrn is an American animator, voice actor, writer, and director best known for his creative leadership on DreamWorks Animation films such as the Trolls franchise.
-
D.
Don Brochu
Don Brochu is a film editor best known for his work on major Hollywood movies, including the hit thriller "The Bodyguard."
-
E.
Harold Wenstrom
Harold Wenstrom was an American cinematographer active during the early 20th century, known for his work on numerous silent and early sound films.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658d92f9c8190b363587ed1881c2c |
completed | April 20, 2026, 4:48 p.m. |
Created at: April 10, 2026, 1:51 p.m.