Triple
T207126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord |
E4632
|
entity |
| Predicate | linguisticallyRelatedTo |
P10003
|
FINISHED |
| Object | Hebrew title Adonai |
—
|
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: Hebrew title Adonai | Statement: [Lord, linguisticallyRelatedTo, Hebrew title Adonai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linguisticallyRelatedTo Context triple: [Lord, linguisticallyRelatedTo, Hebrew title Adonai]
-
A.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
B.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
C.
areMutuallyIntelligibleToSomeDegree
Indicates that two or more languages or communication systems can be at least partially understood by each other’s users without prior learning or translation.
-
D.
hasCognate
Indicates that two linguistic forms in different languages share a common historical origin, typically descending from the same ancestral word.
-
E.
cognate
Indicates that two linguistic forms share a common historical origin, typically deriving from the same ancestral word.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b4c7f908190876c1041db52dffc |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25e292fdc8190bfd51d8848f9ed58 |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.