Triple
T157709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Septuagint |
E3214
|
entity |
| Predicate | hasOriginalLanguage |
P1754
|
FINISHED |
| Object | Biblical Hebrew |
—
|
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: Biblical Hebrew | Statement: [Septuagint, hasOriginalLanguage, Biblical Hebrew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginalLanguage Context triple: [Septuagint, hasOriginalLanguage, Biblical Hebrew]
-
A.
hasLanguageOfOrigin
chosen
Indicates that one entity has its origin or source in the language specified by another entity.
-
B.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
C.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
D.
primaryLanguageOf
Indicates that a specified language is the main or official language used by a particular entity (such as a person, organization, or region).
-
E.
nativeLanguage
Indicates the language that a person or entity originally learned and uses as their primary or first language.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25830136881909f5ecb2cb22097b2 |
completed | Feb. 28, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69a2565f30848190a2a71fdb7dc140b5 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.