Triple
T13496764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German telephone numbering plan |
E320781
|
entity |
| Predicate | numberFormatExample |
P83564
|
FINISHED |
| Object | +49 30 123456 |
—
|
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: +49 30 123456 | Statement: [German telephone numbering plan, numberFormatExample, +49 30 123456]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberFormatExample Context triple: [German telephone numbering plan, numberFormatExample, +49 30 123456]
-
A.
valueFormatExample
chosen
Indicates an example of how a value should be formatted or represented.
-
B.
currencyNumber
Indicates the numerical value or denomination associated with a specific currency.
-
C.
roundFormat
Indicates the specific structure, rules, or style in which a particular round of an event, game, or process is conducted.
-
D.
thousandsSeparator
Indicates the character or symbol used to visually separate groups of three digits in large numbers for readability.
-
E.
currencyAppearance
Indicates how a currency physically looks or is visually represented, such as its design, color, or format.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4e9ca4819083116890a65389f9 |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae06061881909a6a6032e0507587 |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:43 p.m.