Triple
T34176930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Matthew |
E876697
|
entity |
| Predicate | possibleLanguageOfSources |
P2925
|
FINISHED |
| Object | Aramaic |
—
|
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: Aramaic | Statement: [St Matthew, possibleLanguageOfSources, Aramaic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: possibleLanguageOfSources Context triple: [St Matthew, possibleLanguageOfSources, Aramaic]
-
A.
languageOfSources
chosen
Indicates that the specified language is the language in which the referenced sources or source materials are expressed.
-
B.
possibleLanguage
Indicates that an entity could plausibly be expressed, interpreted, or communicated in a given language.
-
C.
hasSourceLanguageForLoanwords
Indicates that a language serves as the original source from which loanwords are borrowed into another language.
-
D.
indirectOriginLanguage
Indicates that something originates from a particular language, not directly but through one or more intermediate languages or sources.
-
E.
possibleLanguageBranch
Indicates that one entity may belong to, derive from, or be classified under the language branch represented by the other entity, without asserting this relationship as certain.
- 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_69f349ad97ac8190bf1f17417c970e64 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: May 1, 2026, 1:54 a.m.