Triple
T28395878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daji |
E719286
|
entity |
| Predicate | associatedMoralTheme |
P25343
|
FINISHED |
| Object | warning against lust and moral corruption |
—
|
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: warning against lust and moral corruption | Statement: [Daji, associatedMoralTheme, warning against lust and moral corruption]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedMoralTheme Context triple: [Daji, associatedMoralTheme, warning against lust and moral corruption]
-
A.
moralTheme
chosen
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
-
B.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept in relation to another.
-
C.
moralAssociation
Indicates a perceived ethical or moral connection between entities, such as one influencing or reflecting the moral character, values, or judgment of the other.
-
D.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
E.
moralBelief
Indicates that an agent holds a normative judgment about what is right, wrong, good, or bad in a given context.
- 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_69eff6efd1b08190ae3cefd4f11388a2 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_6a013e23698c81909a32d371b6f158d0 |
completed | May 11, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_6a013db04b108190985897aa6e95b4ec |
completed | May 11, 2026, 2:23 a.m. |
Created at: April 28, 2026, 1:16 a.m.