Triple
T13513641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jep |
E322699
|
entity |
| Predicate | shortFormOf |
P43
|
FINISHED |
| Object | Jeppe |
E322700
|
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: Jeppe | Statement: [Jep, shortFormOf, Jeppe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeppe Context triple: [Jep, shortFormOf, Jeppe]
-
A.
Jeppe
chosen
Jeppe is a Scandinavian masculine given name, commonly used in Denmark and related to names like Jepser or Jepsen.
-
B.
Jesper
Jesper is a masculine given name commonly used in Scandinavian countries and parts of Europe.
-
C.
Jørgen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
-
D.
Jens
Jens is a masculine given name commonly used in Scandinavian and German-speaking countries, equivalent to "John" in English.
-
E.
Jens
Jens is a small municipality in the canton of Bern in Switzerland, located within the bilingual region around the city of Biel/Bienne.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf87ca288190a147fbdb2f90985f |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75492676c81909602745e2b6436cb |
completed | May 3, 2026, 1:58 p.m. |
Created at: April 9, 2026, 9:44 p.m.