Triple
T3858915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Go with It |
E90086
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Dennis Virkler
Dennis Virkler was an American film editor known for his work on numerous Hollywood features, including major comedies and action films.
|
E502724
|
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: Dennis Virkler | Statement: [Just Go with It, editedBy, Dennis Virkler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dennis Virkler Context triple: [Just Go with It, editedBy, Dennis Virkler]
-
A.
Dennis Lauscha
Dennis Lauscha is an American sports executive who serves as a top leader within the New Orleans professional sports organization, overseeing business operations for the Saints and related ventures.
-
B.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
-
C.
Duane Schuler
Duane Schuler is an American theatrical lighting designer known for his work in opera, including major productions at leading opera houses.
-
D.
Larry Kellner
Larry Kellner is an American business executive best known for leading Continental Airlines as its chief executive officer.
-
E.
Joe Sahlen
Joe Sahlen is an American businessman best known as the owner of the Western New York Flash professional soccer club.
- 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: Dennis Virkler Triple: [Just Go with It, editedBy, Dennis Virkler]
Generated description
Dennis Virkler was an American film editor known for his work on numerous Hollywood features, including major comedies and action films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dennis Virkler Target entity description: Dennis Virkler was an American film editor known for his work on numerous Hollywood features, including major comedies and action films.
-
A.
Dennis Lauscha
Dennis Lauscha is an American sports executive who serves as a top leader within the New Orleans professional sports organization, overseeing business operations for the Saints and related ventures.
-
B.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
-
C.
Duane Schuler
Duane Schuler is an American theatrical lighting designer known for his work in opera, including major productions at leading opera houses.
-
D.
Larry Kellner
Larry Kellner is an American business executive best known for leading Continental Airlines as its chief executive officer.
-
E.
Joe Sahlen
Joe Sahlen is an American businessman best known as the owner of the Western New York Flash professional soccer club.
- 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_69aed95b3c088190a8f85d19e6070599 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec1e68f88190941c39221486f6ae |
completed | March 9, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beef70e6048190b0e5659a59634d5d |
completed | March 21, 2026, 7:20 p.m. |
| NEDg | Description generation | batch_69bef031c0e881908a4a427788db618f |
completed | March 21, 2026, 7:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bef086d9d08190813cd1bd8fdec5d3 |
completed | March 21, 2026, 7:24 p.m. |
Created at: March 9, 2026, 3:19 p.m.