Triple
T32887891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schultze |
E841249
|
entity |
| Predicate | hasFrequencyTypeInGermany |
P28499
|
FINISHED |
| Object | medium common |
—
|
LITERAL 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: medium common | Statement: [Schultze, hasFrequencyTypeInGermany, medium common]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyTypeInGermany Context triple: [Schultze, hasFrequencyTypeInGermany, medium common]
-
A.
hasApproximateEuropeanFrequency
Indicates that the subject has an estimated or approximate occurrence rate within European populations.
-
B.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
C.
hasFrequencyNote
Indicates that something is associated with a specific note describing how often it occurs or is repeated.
-
D.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
E.
hasFrequencyChannels
Indicates that an entity is associated with, or operates over, one or more specific frequency channels.
- 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_69f349446e288190a70c05bcc4d81172 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
Created at: May 1, 2026, 1:18 a.m.