Triple
T20921267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | N scale |
E515213
|
entity |
| Predicate | letterDesignationOrigin |
P5539
|
FINISHED |
| Object | N comes from the German word "Neun" meaning nine |
—
|
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: N comes from the German word "Neun" meaning nine | Statement: [N scale, letterDesignationOrigin, N comes from the German word "Neun" meaning nine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: letterDesignationOrigin Context triple: [N scale, letterDesignationOrigin, N comes from the German word "Neun" meaning nine]
-
A.
collectionDesignation
Indicates that one entity assigns or specifies a particular collection label or designation for another entity.
-
B.
hasLetterDesignation
chosen
Indicates that an entity is assigned or associated with a specific letter-based designation or code.
-
C.
genomeDesignation
Indicates the specific label or identifier assigned to a particular genome within a biological or genomic context.
-
D.
exampleDesignation
Indicates that one entity is identified or labeled as a representative or illustrative instance of another entity.
-
E.
designationFor
Indicates that one entity serves as the official title, label, or role name assigned to another entity.
- 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f64e56bc81908aa86f2176fcc0db |
completed | April 21, 2026, 4 a.m. |
| PD | Predicate disambiguation | batch_69e5c9af1fe08190953366a466950140 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:48 p.m.