Triple
T7905706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebenezer Scrooge |
E183569
|
entity |
| Predicate | moralLessonAssociatedWith |
P57332
|
FINISHED |
| Object | importance of generosity |
—
|
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: importance of generosity | Statement: [Ebenezer Scrooge, moralLessonAssociatedWith, importance of generosity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralLessonAssociatedWith Context triple: [Ebenezer Scrooge, moralLessonAssociatedWith, importance of generosity]
-
A.
moralOfMyth
Indicates the underlying lesson, ethical teaching, or message conveyed by a myth.
-
B.
hasMoralMessage
chosen
Indicates that something conveys or embodies a lesson, value, or guidance about what is right or wrong behavior.
-
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.
moralExemplarOf
Indicates that one entity serves as a model or standard of moral behavior for another entity or group.
-
E.
moralTheme
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a56c9f0819094dc87fe55a8823e |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:03 p.m.