Triple
T1747867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joker (2019 film) |
E38375
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Jeff Groth
Jeff Groth is a film editor best known for his work on the critically acclaimed 2019 psychological thriller "Joker."
|
E287267
|
NE FINISHED |
How this triple was built (4 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 Groth | Statement: [Joker (2019 film), editedBy, Jeff Groth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Groth Context triple: [Joker (2019 film), editedBy, Jeff Groth]
-
A.
Mike Gunton
Mike Gunton is a British television producer best known for his work on landmark BBC natural history documentaries.
-
B.
Mark Okerstrom
Mark Okerstrom is a Canadian business executive best known for serving as the former CEO of Expedia Group.
-
C.
Michael Nylander
Michael Nylander is a Swedish former professional ice hockey center who played over 900 NHL games and was known for his playmaking skills with teams such as the New York Rangers and Washington Capitals.
-
D.
Matt Graver
Matt Graver is a seasoned and morally ambiguous CIA operative who orchestrates covert operations against Mexican drug cartels in the film "Sicario."
-
E.
Rob Hoffman
Rob Hoffman is a music producer best known for his work in hip-hop and R&B, including collaborations with prominent artists in the genre.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jeff Groth Triple: [Joker (2019 film), editedBy, Jeff Groth]
Generated description
Jeff Groth is a film editor best known for his work on the critically acclaimed 2019 psychological thriller "Joker."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeff Groth Target entity description: Jeff Groth is a film editor best known for his work on the critically acclaimed 2019 psychological thriller "Joker."
-
A.
Mike Gunton
Mike Gunton is a British television producer best known for his work on landmark BBC natural history documentaries.
-
B.
Mark Okerstrom
Mark Okerstrom is a Canadian business executive best known for serving as the former CEO of Expedia Group.
-
C.
Michael Nylander
Michael Nylander is a Swedish former professional ice hockey center who played over 900 NHL games and was known for his playmaking skills with teams such as the New York Rangers and Washington Capitals.
-
D.
Matt Graver
Matt Graver is a seasoned and morally ambiguous CIA operative who orchestrates covert operations against Mexican drug cartels in the film "Sicario."
-
E.
Rob Hoffman
Rob Hoffman is a music producer best known for his work in hip-hop and R&B, including collaborations with prominent artists in the genre.
- F. None of above. chosen
Provenance (5 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63ecda0c819091f81942a5bde31d |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa035474881908e283cd1af65beea |
completed | March 10, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69afa1196aac81909b25557dff5acf5e |
completed | March 10, 2026, 4:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afa1a7d9b48190a8b14a7d209e1f26 |
completed | March 10, 2026, 4:44 a.m. |
Created at: March 4, 2026, 7:31 p.m.