Triple
T11279574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne |
E267026
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Anke |
E482260
|
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: Anke | Statement: [Anne, hasVariant, Anke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anke Context triple: [Anne, hasVariant, Anke]
-
A.
Anja
Anja is a feminine given name commonly used in various European countries, often considered a variant of Anna.
-
B.
Anneke
chosen
Anneke is a feminine given name of Dutch origin, commonly used in the Netherlands and other Germanic-language regions.
-
C.
Annelise
Annelise is the given name of Anni Albers, the influential German-born textile artist and printmaker associated with the Bauhaus and later American modernism.
-
D.
Annis
Annis is a feminine given name of English origin, historically used in the Anglophone world.
-
E.
Anette
Anette is a feminine given name, commonly used in various European countries and considered a variant of names like Annette or Annette-derived forms.
- 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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e969b3448190940e2bd499d2d7de |
completed | April 9, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4f46308348190a47f73030cae0be5 |
completed | April 19, 2026, 3:27 p.m. |
Created at: April 8, 2026, 9:31 p.m.