Triple
T8357673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the Adversary |
E196718
|
entity |
| Predicate | moralValence |
P46135
|
FINISHED |
| Object | negative |
—
|
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: negative | Statement: [the Adversary, moralValence, negative]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralValence Context triple: [the Adversary, moralValence, negative]
-
A.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
B.
moralTone
chosen
Indicates the evaluative moral quality or ethical character expressed in or associated with an action, statement, or situation.
-
C.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept in relation to another.
-
D.
moralCriterion
Indicates that something is being evaluated or classified according to a standard of moral judgment or ethical rightness.
-
E.
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.
- 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_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb804b57f88190907a4e4e389caf5f |
completed | March 31, 2026, 8:05 a.m. |
| PD | Predicate disambiguation | batch_69cb70ca25548190b0f90c5384e3fb3c |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:59 p.m.