Triple
T13513663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeppe |
E322700
|
entity |
| Predicate | shortFormOf |
P43
|
FINISHED |
| Object | Jesper |
E593969
|
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: Jesper | Statement: [Jeppe, shortFormOf, Jesper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jesper Context triple: [Jeppe, shortFormOf, Jesper]
-
A.
Jesper
chosen
Jesper is a masculine given name commonly used in Scandinavian countries and parts of Europe.
-
B.
Johan
Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
-
C.
Johan
Johan is the given first name of the Swedish playwright and novelist August Strindberg.
-
D.
Johan
Johan is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
-
E.
Jeppe
Jeppe is a Scandinavian masculine given name, commonly used in Denmark and related to names like Jepser or Jepsen.
- 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_69f78ad8903c8190afbf15234a81d657 |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:44 p.m.