Triple
T28761400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D'Angelo Barksdale |
E726123
|
entity |
| Predicate | moralTurningPoint |
P167377
|
FINISHED |
| Object | reacts strongly to Wallace's death |
—
|
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: reacts strongly to Wallace's death | Statement: [D'Angelo Barksdale, moralTurningPoint, reacts strongly to Wallace's death]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralTurningPoint Context triple: [D'Angelo Barksdale, moralTurningPoint, reacts strongly to Wallace's death]
-
A.
moralTrajectory
Indicates the direction and pattern of change in an entity’s moral behavior or ethical stance over time.
-
B.
moralTheme
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
-
C.
moralCriterion
Indicates that something is being evaluated or classified according to a standard of moral judgment or ethical rightness.
-
D.
derivesMoralityFrom
Indicates that one entity bases or grounds its moral principles, judgments, or ethical framework on another entity.
-
E.
hasMoralPerspective
Indicates that an entity holds or applies a particular moral or ethical viewpoint in evaluating actions, situations, or other entities.
- 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_69f03198be14819098fa74e48b3749bf |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f66a6468ec8190a43ed6cd8c797f42 |
completed | May 2, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f6659b62fc8190b21555d0ba54db2d |
completed | May 2, 2026, 8:59 p.m. |
| PDg | Predicate description generation | batch_69f6691da93081909deaf680614fc900 |
completed | May 2, 2026, 9:14 p.m. |
Created at: April 28, 2026, 6:11 a.m.