Triple
T1891927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novalis |
E41891
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Georg |
E56084
|
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: Georg | Statement: [Novalis, givenName, Georg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georg Context triple: [Novalis, givenName, Georg]
-
A.
Georg
chosen
Georg is the given first name of the renowned German mathematician Bernhard Riemann.
-
B.
Georg-Hans
Georg-Hans is the given name of Georg-Hans Reinhardt, a German general who served in the Wehrmacht during World War II.
-
C.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Georgy
Georgy is a masculine given name of Russian origin, notably borne by Soviet military commander Georgy Zhukov.
-
E.
Gustav
Gustav is a masculine given name of German origin, borne by several notable historical figures including scientists, artists, and royalty.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb145f96c8190a71bb9e442892e68 |
completed | March 7, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeae9e1f4819082afdd3b8e065c01 |
completed | March 8, 2026, 9:32 p.m. |
Created at: March 4, 2026, 7:34 p.m.