Triple
T14934283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George |
E372348
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Jürgen |
E140183
|
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: Jürgen | Statement: [George, hasVariant, Jürgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jürgen Context triple: [George, hasVariant, Jürgen]
-
A.
Jürgen
chosen
Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
-
B.
Jörg
Jörg is a masculine given name of German origin, commonly used in German-speaking countries.
-
C.
Rudi Jäger
Rudi Jäger is a sadistic Nazi prison warden and antagonist in the video game Wolfenstein: The Old Blood, known for hunting the protagonist with his attack dogs.
-
D.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
E.
Jürgen Knieper
Jürgen Knieper is a German composer best known for his film and television scores, including work on notable German productions and international art-house 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e8c8d188190b027f14256b0ce01 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:37 a.m.