Triple
T465888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miserando atque eligendo |
E8445
|
entity |
| Predicate | translationApproximate |
P15005
|
FINISHED |
| Object | by having mercy and by choosing |
—
|
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: by having mercy and by choosing | Statement: [Miserando atque eligendo, translationApproximate, by having mercy and by choosing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translationApproximate Context triple: [Miserando atque eligendo, translationApproximate, by having mercy and by choosing]
-
A.
translationMethod
Indicates the technique or process used to translate content from one language or form to another.
-
B.
translator
Indicates that one entity serves to convert or render content from one language or form into another for a second entity.
-
C.
otherLanguage
Indicates that an entity has or uses an additional language distinct from its primary or main language.
-
D.
languageShift
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
E.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
- F. None of above. chosen
Provenance (4 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_69a2e7f3aeb48190a19453e3a043f486 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efd6ec708190b78c7f22deb3ca64 |
completed | Feb. 28, 2026, 1:38 p.m. |
| PD | Predicate disambiguation | batch_69a2edea1acc81908a72d9f4c43438ea |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2ef611b9c8190ac5e9174744d9127 |
completed | Feb. 28, 2026, 1:36 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.