Triple
T26557099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Dravot |
E666138
|
entity |
| Predicate | moralLessonAssociation |
P57332
|
FINISHED |
| Object | consequences of hubris |
—
|
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: consequences of hubris | Statement: [Daniel Dravot, moralLessonAssociation, consequences of hubris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralLessonAssociation Context triple: [Daniel Dravot, moralLessonAssociation, consequences of hubris]
-
A.
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.
-
B.
moralImplication
Indicates that one situation, action, or state of affairs entails or suggests a particular moral judgment, obligation, or ethical consequence.
-
C.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept in relation to another.
-
D.
hasMoralMessage
chosen
Indicates that something conveys or embodies a lesson, value, or guidance about what is right or wrong behavior.
-
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_69ee9cf7e94481909f0d556b36e43572 |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f6247480cc8190a887eedaeb94615c |
completed | May 2, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69f623a7539c8190b71797f583da9f63 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 1:50 a.m.