Triple
T23988715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PICA format |
E605005
|
entity |
| Predicate | mainLanguageContext |
P8383
|
FINISHED |
| Object | German |
—
|
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: German | Statement: [PICA format, mainLanguageContext, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainLanguageContext Context triple: [PICA format, mainLanguageContext, German]
-
A.
nativeLanguageContext
Indicates the relationship in which a language functions as the primary or native linguistic context for an entity’s communication or interpretation.
-
B.
originalLanguageContext
Indicates the language in which something was first created or expressed, providing the original linguistic context for its content or meaning.
-
C.
secondaryLanguageContext
Indicates that the associated information, interaction, or content occurs within or is tailored to a secondary (non-primary) language setting or usage context.
-
D.
hasLanguageContext
chosen
Indicates that an entity is associated with or interpreted within a specific language or linguistic context.
-
E.
currentLanguageSituation
Indicates the language currently being used or in effect in a given context or situation.
- 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_69e295463f7c8190b1c19dbd114641b9 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d38902fc8190af51cedfce1c6c13 |
completed | April 29, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69f1615994c48190a5de95d3f7e5cd0a |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:37 p.m.