Triple
T14345029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Octopus Teacher |
E355692
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Dan Schwalm |
E355692
|
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: Dan Schwalm | Statement: [My Octopus Teacher, editor, Dan Schwalm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Schwalm Context triple: [My Octopus Teacher, editor, Dan Schwalm]
-
A.
Dan Schwalm
chosen
Dan Schwalm is a film editor best known for his work on the Academy Award–winning documentary "My Octopus Teacher."
-
B.
John Diehl
John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
-
C.
John Dierkes
John Dierkes was an American character actor known for his tall, gaunt appearance and roles in mid-20th-century Westerns and war films.
-
D.
Fred Kohlmar
Fred Kohlmar was an American film producer active during Hollywood's studio era, known for overseeing a variety of popular comedies and musicals.
-
E.
Thomas Wiegand
Thomas Wiegand is a German electrical engineer and video coding expert best known for his leading role in developing the H.264/AVC video compression standard.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e89ed9c8190acdb647ee618e919 |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb73334c8190a96a92d199c3b101 |
completed | May 8, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:14 a.m.