Triple
T15530343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Devo |
E370196
|
entity |
| Predicate | member |
P10
|
FINISHED |
| Object |
Jeff Friedl
Jeff Friedl is an American drummer best known for his work with alternative rock and industrial bands, including his role in Devo’s later lineups.
|
E1162568
|
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 Friedl | Statement: [Devo, member, Jeff Friedl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Friedl Context triple: [Devo, member, Jeff Friedl]
-
A.
David Flanagan
David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
-
B.
Michael Feathers
Michael Feathers is a software engineer, consultant, and author known for his influential work on legacy code, refactoring, and improving software design and maintainability.
-
C.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
-
D.
Michael Kaplan
Michael Kaplan is a composer and musician known for creating the music for the film "Burlesque."
-
E.
L. Peter Deutsch
L. Peter Deutsch is a computer scientist and software developer best known for creating the Ghostscript interpreter for the PostScript language and PDF files.
- 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 Friedl Triple: [Devo, member, Jeff Friedl]
Generated description
Jeff Friedl is an American drummer best known for his work with alternative rock and industrial bands, including his role in Devo’s later lineups.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeff Friedl Target entity description: Jeff Friedl is an American drummer best known for his work with alternative rock and industrial bands, including his role in Devo’s later lineups.
-
A.
David Flanagan
David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
-
B.
Michael Feathers
Michael Feathers is a software engineer, consultant, and author known for his influential work on legacy code, refactoring, and improving software design and maintainability.
-
C.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
-
D.
Michael Kaplan
Michael Kaplan is a composer and musician known for creating the music for the film "Burlesque."
-
E.
L. Peter Deutsch
L. Peter Deutsch is a computer scientist and software developer best known for creating the Ghostscript interpreter for the PostScript language and PDF files.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0414773548190b3311515f9d957dd |
completed | April 16, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d5b989c8190a76612df167ba1dd |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3ea7d5ac81908bd1ee64de39dba7 |
completed | May 9, 2026, 2:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff413a68488190a6c8907e36a602dc |
completed | May 9, 2026, 2:14 p.m. |
Created at: April 10, 2026, 4:05 a.m.