Triple
T19988155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amy Juergens |
E493988
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Juergens |
—
|
NE NERFINISHED |
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: Juergens | Statement: [Amy Juergens, familyName, Juergens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juergens Context triple: [Amy Juergens, familyName, Juergens]
-
A.
Jürgens
chosen
Jürgens is a German surname most notably associated with the Austrian-German actor Curt Jürgens.
-
B.
Jurgenson
Jurgenson was a prominent Russian music publishing house known for issuing major works by composers such as Tchaikovsky.
-
C.
Jurgensen
Jurgensen is the surname of Sonny Jurgensen, a Hall of Fame American football quarterback best known for his prolific passing career with the Washington Redskins.
-
D.
Jungmann
Jungmann is the nickname of the Bücker Bü 131, a German 1930s biplane widely used as a primary trainer aircraft before and during World War II.
-
E.
Jörg
Jörg is a masculine given name of German origin, commonly used in German-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fddfee081909aa8cd6e279b2a7a |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:30 p.m.