Triple
T9984283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | people of Madyan |
E196526
|
entity |
| Predicate | moralLessonAbout |
P57332
|
FINISHED |
| Object | honesty in business |
—
|
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: honesty in business | Statement: [people of Madyan, moralLessonAbout, honesty in business]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralLessonAbout Context triple: [people of Madyan, moralLessonAbout, honesty in business]
-
A.
moralTheme
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
-
B.
moralOfMyth
Indicates the underlying lesson, ethical teaching, or message conveyed by a myth.
-
C.
hasMoralMessage
chosen
Indicates that something conveys or embodies a lesson, value, or guidance about what is right or wrong behavior.
-
D.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept in relation to another.
-
E.
derivesMoralityFrom
Indicates that one entity bases or grounds its moral principles, judgments, or ethical framework on another entity.
- 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_69ca82efbce081908179b4b9c65096eb |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb8bdc0388190bbbd4bdc5ac3adec |
completed | April 2, 2026, 12:30 a.m. |
| PD | Predicate disambiguation | batch_69cd1da07db88190945bcdab3ca82e71 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:49 p.m.