Triple
T6598536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Studebaker Avanti |
E148537
|
entity |
| Predicate | designer |
P184
|
FINISHED |
| Object |
John Ebstein
John Ebstein was an automotive designer best known for his work on the distinctive Studebaker Avanti sports coupe of the early 1960s.
|
E607202
|
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: John Ebstein | Statement: [Studebaker Avanti, designer, John Ebstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Ebstein Context triple: [Studebaker Avanti, designer, John Ebstein]
-
A.
Michael Stein
Michael Stein is an American musician and composer best known for co-creating the synth-driven score for the hit Netflix series "Stranger Things" as part of the electronic band Survive.
-
B.
Philip Stein
Philip Stein is a brand best known for its luxury wristwatches and lifestyle accessories that incorporate wellness-focused technologies.
-
C.
E. H. Hansen
E. H. Hansen is an audio professional known for work on the sound recording of the film "The Rains Came."
-
D.
Alex Gottlieb
Alex Gottlieb was an American film producer and screenwriter active during Hollywood’s mid-20th-century studio era.
-
E.
Ben Epstein
Ben Epstein is an ambitious young New Yorker and aspiring fashion entrepreneur who serves as one of the central protagonists in the HBO series "How to Make It in America."
- 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: John Ebstein Triple: [Studebaker Avanti, designer, John Ebstein]
Generated description
John Ebstein was an automotive designer best known for his work on the distinctive Studebaker Avanti sports coupe of the early 1960s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Ebstein Target entity description: John Ebstein was an automotive designer best known for his work on the distinctive Studebaker Avanti sports coupe of the early 1960s.
-
A.
Michael Stein
Michael Stein is an American musician and composer best known for co-creating the synth-driven score for the hit Netflix series "Stranger Things" as part of the electronic band Survive.
-
B.
Philip Stein
Philip Stein is a brand best known for its luxury wristwatches and lifestyle accessories that incorporate wellness-focused technologies.
-
C.
E. H. Hansen
E. H. Hansen is an audio professional known for work on the sound recording of the film "The Rains Came."
-
D.
Alex Gottlieb
Alex Gottlieb was an American film producer and screenwriter active during Hollywood’s mid-20th-century studio era.
-
E.
Ben Epstein
Ben Epstein is an ambitious young New Yorker and aspiring fashion entrepreneur who serves as one of the central protagonists in the HBO series "How to Make It in America."
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aeeffdf0819090af7bba918bef84 |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e43224dc81909dea493a5ee2726e |
completed | March 27, 2026, 8:10 p.m. |
| NEDg | Description generation | batch_69c6e4e9c344819099ad11c21c2e4a6e |
completed | March 27, 2026, 8:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e581a4f88190b1f64033a49d1bae |
completed | March 27, 2026, 8:16 p.m. |
Created at: March 27, 2026, 1:56 p.m.