Triple
T7905813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marwencol |
E183572
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Jeff Malmberg |
E207059
|
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: Jeff Malmberg | Statement: [Marwencol, writer, Jeff Malmberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Malmberg Context triple: [Marwencol, writer, Jeff Malmberg]
-
A.
Jeff Malmberg
chosen
Jeff Malmberg is an American documentary filmmaker best known for directing the acclaimed film "Marwencol," which inspired the narrative of "Welcome to Marwen."
-
B.
Greg Eklund
Greg Eklund is an American drummer best known for his work with the alternative rock band Everclear.
-
C.
Jon Ekstrand
Jon Ekstrand is a Swedish film composer and sound designer known for his atmospheric scores for documentaries and feature films, including collaborations with director Daniel Espinosa.
-
D.
Jeffrey Nordling
Jeffrey Nordling is an American actor known for his work in television dramas and films, often portraying complex professional and family-man characters.
-
E.
Michael Eklund
Michael Eklund is a Canadian character actor known for his intense, often villainous roles in film and television thrillers.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a56c9f0819094dc87fe55a8823e |
completed | March 31, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63a63fe0819095778a12bf437cf4 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 5:03 p.m.