Triple
T3471453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albrecht |
E73267
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Albrechtus |
E73267
|
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: Albrechtus | Statement: [Albrecht, hasVariant, Albrechtus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albrechtus Context triple: [Albrecht, hasVariant, Albrechtus]
-
A.
Aribert
Aribert is a Germanic given name, historically borne by medieval nobles and clergy, derived from elements meaning "army" and "bright."
-
B.
Berthold
Berthold is the family name of American comedian and actress Kate McKinnon, known for her work on Saturday Night Live.
-
C.
Albrecht
chosen
Albrecht is a Germanic given name, historically borne by various nobles, artists, and scholars in German-speaking Europe.
-
D.
Gebhard
Gebhard is a German given name most famously borne by Gebhard Leberecht von Blücher, the Prussian field marshal who helped defeat Napoleon at Waterloo.
-
E.
Winrich von Kniprode
Winrich von Kniprode was a 14th-century Grand Master of the Teutonic Order whose long rule marked the height of the order’s political power and cultural influence in Prussia.
- 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_69ad85b2fed48190948c8765e453d270 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb3af0cc81909e575828caeaeae0 |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e5c1f008190b1351e3d3e53fc11 |
completed | March 13, 2026, 3:02 a.m. |
Created at: March 8, 2026, 3:17 p.m.