Triple
T23388480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tantum ergo |
E593947
|
entity |
| Predicate | hasCommonTranslationLanguage |
P152066
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Tantum ergo, hasCommonTranslationLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonTranslationLanguage Context triple: [Tantum ergo, hasCommonTranslationLanguage, English]
-
A.
hasRelatedLanguage
Indicates that one language is related to another through shared linguistic origins, features, or classification.
-
B.
hasLanguageSimilarTo
Indicates that one entity uses or is associated with a language that is similar or closely related to the language used or associated with another entity.
-
C.
hasTranslation
Indicates that one entity is a translation or translated version of another entity in a different language.
-
D.
canTranslateBetween
Indicates that an entity has the ability to translate or convert information accurately between two specified languages, formats, or representation systems.
-
E.
sharesLanguageWith
Indicates that two entities use at least one common language for communication.
- 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_69e25d2754fc819085deea939bde60ab |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a499bad88190afca1afb2e3fddb0 |
completed | April 29, 2026, 6:26 a.m. |
| PD | Predicate disambiguation | batch_69f061dde2e481908308952f9c0d3c2e |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:35 p.m.