Triple
T935281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Book of Leviticus |
E20180
|
entity |
| Predicate | hebrewTitleMeaning |
P4542
|
FINISHED |
| Object | “And He called” |
—
|
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: “And He called” | Statement: [Book of Leviticus, hebrewTitleMeaning, “And He called”]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hebrewTitleMeaning Context triple: [Book of Leviticus, hebrewTitleMeaning, “And He called”]
-
A.
titleMeaning
chosen
Indicates that one entity expresses or explains the meaning, significance, or interpretation of another entity’s title.
-
B.
hasNameInHebrew
Indicates that an entity is associated with a specific name expressed in the Hebrew language.
-
C.
titleInEnglish
Indicates that an entity’s title or name is given in the English language.
-
D.
hasTitleEtymology
Indicates that the origin or derivation of a title is based on, or explained by, another term, name, or linguistic source.
-
E.
titleInJapanese
Indicates that one entity is the title of another entity expressed specifically in the Japanese 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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b363ea5c819098ec1d87f785bad4 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b29b245c8190b143f28b77fede3c |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.