Triple
T953672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muspilli |
E20577
|
entity |
| Predicate | sourceLanguageInfluence |
P2925
|
FINISHED |
| Object | Latin biblical texts |
—
|
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: Latin biblical texts | Statement: [Muspilli, sourceLanguageInfluence, Latin biblical texts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sourceLanguageInfluence Context triple: [Muspilli, sourceLanguageInfluence, Latin biblical texts]
-
A.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
B.
sourceLanguageMeaning
Indicates that one entity expresses the meaning or sense of another entity in a particular source language.
-
C.
hasLexicalInfluenceOn
Indicates that one linguistic element (such as a word, phrase, or lexicon) has affected or shaped the form, usage, or meaning of another linguistic element.
-
D.
influencedLanguageFamily
Indicates that one language family has had a significant impact on the development, structure, or usage of another language family.
-
E.
languageOfSources
chosen
Indicates that the specified language is the language in which the referenced sources or source materials are expressed.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3d8f2e0819097554a301f8aa70f |
completed | March 1, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a045308190ab94f3adab40db8d |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.