Triple
T21531308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalends |
E531235
|
entity |
| Predicate | influenceOnLanguages |
P108920
|
FINISHED |
| Object | medieval Latin timekeeping terminology |
—
|
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: medieval Latin timekeeping terminology | Statement: [Kalends, influenceOnLanguages, medieval Latin timekeeping terminology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influenceOnLanguages Context triple: [Kalends, influenceOnLanguages, medieval Latin timekeeping terminology]
-
A.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
B.
influencesLanguageOf
Indicates that one entity affects, shapes, or alters the language used by another entity.
-
C.
languageOfInfluence
Indicates a relationship where one language has influenced the development, usage, or characteristics of another language.
-
D.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
E.
linguisticInfluence
chosen
Indicates that one entity has affected, shaped, or contributed to the language, style, or linguistic features of 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d08ebf881909574e098404f93fa |
completed | April 26, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:27 p.m.