Triple
T23973890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenzsche Regel |
E604308
|
entity |
| Predicate | hatEntsprechungInEnglisch |
P55633
|
FINISHED |
| Object | Lenz's law |
—
|
NE NERFINISHED |
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: Lenz's law | Statement: [Lenzsche Regel, hatEntsprechungInEnglisch, Lenz's law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hatEntsprechungInEnglisch Context triple: [Lenzsche Regel, hatEntsprechungInEnglisch, Lenz's law]
-
A.
hasEnglishGloss
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
-
B.
counterpartEnglishName
chosen
Indicates that an entity has a corresponding counterpart whose name is given in English.
-
C.
correspondsToAbbreviationInGerman
Indicates that one entity is the full form or concept for which the other entity serves as the corresponding abbreviation in the German language.
-
D.
meaningInGerman
Indicates that one entity expresses the meaning or translation of another entity in the German language.
-
E.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
- F. None of above.
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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1dda91c8190af716bceb3225aee |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:26 p.m.