Triple
T4649158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worner |
E102246
|
entity |
| Predicate | hasSpellingVariant |
P457
|
FINISHED |
| Object | Wörner |
E102246
|
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: Wörner | Statement: [Worner, hasSpellingVariant, Wörner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wörner Context triple: [Worner, hasSpellingVariant, Wörner]
-
A.
Worner
chosen
Worner is a surname and variant spelling of "Warner," used by various individuals and families, particularly in English-speaking countries.
-
B.
Ruländer
Ruländer is a traditional German name for the Pinot Gris grape variety, commonly used for rich, full-bodied white wines.
-
C.
Suter
Suter is a surname of Germanic origin, often associated with individuals of Swiss or German heritage.
-
D.
Mennekes
Mennekes is a German electrical engineering company best known in e-mobility for developing the widely adopted Type 2 AC charging connector for electric vehicles.
-
E.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6300a3fc8190b39ee96d756a748e |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfae3d8f4819082ec002bc4d9819d |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:14 p.m.