Triple
T23752346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gospel of Luke 23:38 |
E587003
|
entity |
| Predicate | languageMentioned |
P69046
|
FINISHED |
| Object | Greek |
—
|
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: Greek | Statement: [Gospel of Luke 23:38, languageMentioned, Greek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageMentioned Context triple: [Gospel of Luke 23:38, languageMentioned, Greek]
-
A.
languageDiscussedIn
chosen
Indicates that a particular language is the topic of discussion within a specified context, source, or discourse.
-
B.
languageEmphasizes
Indicates that one language or linguistic system places particular focus, importance, or prominence on a specific feature, concept, or element compared to others.
-
C.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
D.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
E.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
- 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_69e2490a0eec81908cdef8a862828d7a |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bcc3a06c81908e8a6e1f531c73bc |
completed | April 29, 2026, 8:09 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:13 p.m.