Triple
T7219130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German gold mark |
E150210
|
entity |
| Predicate | hasPluralForm |
P5088
|
FINISHED |
| Object | Goldmark |
E386331
|
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: Goldmark | Statement: [German gold mark, hasPluralForm, Goldmark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goldmark Context triple: [German gold mark, hasPluralForm, Goldmark]
-
A.
Goldmark
chosen
Goldmark was the official gold-backed currency of the German Empire from 1873 until the end of World War I.
-
B.
Gütermann
Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
-
C.
Hammann
Hammann is a German-origin surname borne by various notable individuals in fields such as aviation, music, and academia.
-
D.
Kalmus
Kalmus is a surname most notably associated with Herbert Kalmus, the co-founder of the pioneering color motion picture company Technicolor.
-
E.
Ruländer
Ruländer is a traditional German name for the Pinot Gris grape variety, commonly used for rich, full-bodied white wines.
- 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_69c687effb44819092b95d07d0368c9f |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e9b1a7908190bd215ffb84592e32 |
completed | March 27, 2026, 8:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cc014fb88190818e12b7abe90c0a |
completed | March 28, 2026, 12:39 p.m. |
Created at: March 27, 2026, 2:53 p.m.