Triple
T3887695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Selling of Joseph |
E92982
|
entity |
| Predicate | usesBiblicalNarrative |
P6847
|
FINISHED |
| Object | story of Joseph sold into slavery |
—
|
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: story of Joseph sold into slavery | Statement: [The Selling of Joseph, usesBiblicalNarrative, story of Joseph sold into slavery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesBiblicalNarrative Context triple: [The Selling of Joseph, usesBiblicalNarrative, story of Joseph sold into slavery]
-
A.
hasBiblicalConnection
Indicates a relationship in which one entity is connected to another through references, themes, origins, or influences derived from the Bible.
-
B.
scripturalUse
Indicates that one entity is used as a scriptural reference, source, or basis within the context or content of another entity.
-
C.
containsNarrativeOf
chosen
Indicates that one entity includes or presents the story, account, or narrative content of another entity.
-
D.
containsParable
Indicates that one entity includes or incorporates a parable within it.
-
E.
hasScripture
Indicates that one entity possesses, is associated with, or is defined by a particular scripture or set of scriptural texts.
- 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeecabe3548190a5cbf9d0af0bcfb6 |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.