Triple
T16323743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Kluge |
E396362
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | John Werner Kluge |
E396362
|
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: John Werner Kluge | Statement: [John Kluge, fullName, John Werner Kluge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Werner Kluge Context triple: [John Kluge, fullName, John Werner Kluge]
-
A.
John Kluge
chosen
John Kluge was an American entrepreneur and media mogul best known for building the Metromedia broadcasting empire and becoming one of the richest individuals in the United States.
-
B.
Carl Schenkel
Carl Schenkel was a Swiss film director known for his work on thrillers and adventure films in both European and Hollywood cinema.
-
C.
John Clarence Karcher
John Clarence Karcher was an American geophysicist and pioneer of reflection seismology whose work helped lay the foundations of modern petroleum exploration.
-
D.
Frank Heinricht
Frank Heinricht is a German business executive best known as the long-serving CEO of the specialty glass and materials technology company Schott AG.
-
E.
Jack Couffer
Jack Couffer is an American cinematographer and filmmaker best known for his nature and wildlife photography in documentaries and feature films.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b82fe88190a448597b7827f859 |
completed | April 17, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00260ab4208190900f21cb4f08f926 |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:06 a.m.