Triple
T724016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franconian Jerusalem |
E14680
|
entity |
| Predicate | hasAlternativeNameLanguage |
P4705
|
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: [Franconian Jerusalem, hasAlternativeNameLanguage, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlternativeNameLanguage Context triple: [Franconian Jerusalem, hasAlternativeNameLanguage, German]
-
A.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
B.
hasOfficialNameInEnglish
Indicates that an entity has an officially recognized name expressed in the English language.
-
C.
hasExonym
chosen
Indicates that one entity is known by an alternative name or designation in another language or cultural context.
-
D.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
E.
hasEndonym
Indicates that an entity has a name or designation used by native speakers or within its own local language or community.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5a6ab508190b70a05a9d77829a5 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f700cc81908c6de3eedf68433c |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.