Triple
T7051772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prophet Joseph |
E163982
|
entity |
| Predicate | resistedTemptationFrom |
P74750
|
FINISHED |
| Object | wife of the Egyptian official |
—
|
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: wife of the Egyptian official | Statement: [Prophet Joseph, resistedTemptationFrom, wife of the Egyptian official]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resistedTemptationFrom Context triple: [Prophet Joseph, resistedTemptationFrom, wife of the Egyptian official]
-
A.
firstTemptation
Indicates the initial instance in which an entity is enticed or urged toward a potentially wrong, risky, or forbidden action.
-
B.
hasNumberOfTemptations
Indicates the specific count of temptations associated with an entity or situation.
-
C.
secondTemptation
Indicates a relationship where an entity is subjected to or engages in a second instance of temptation, following an initial tempting event.
-
D.
thirdTemptation
Indicates the relationship in which an entity is subjected to or involved in the third in a sequence of temptations or tests.
-
E.
resistedIn
Indicates that an entity actively opposed, withstood, or fought against something within a specific context, event, or location.
- 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_69c68861678881909961ddf4d779f750 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bdc1f08190975fcdbbb1854d1e |
completed | March 27, 2026, 7:59 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:37 p.m.