Triple
T4128857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bühler |
E84991
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Buehler |
E84991
|
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: Buehler | Statement: [Bühler, hasVariant, Buehler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buehler Context triple: [Bühler, hasVariant, Buehler]
-
A.
Bühler
chosen
Bühler is a German-language surname borne by various notable individuals across fields such as politics, sports, and academia.
-
B.
Kehler
Kehler is a German-origin surname borne by various individuals, including American Air Force general C. Robert Kehler.
-
C.
Oberholtzer
Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
-
D.
Dellner
Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
-
E.
Huber
Huber is a surname of German origin that is borne by various notable individuals across fields such as science, sports, and the arts.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af021c5ca48190a829bab07dda55d0 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576bc48f0819081ac3f921736854f |
completed | March 14, 2026, 2:54 p.m. |
Created at: March 9, 2026, 3:42 p.m.