Triple
T946164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayor Larry Vaughn |
E20416
|
entity |
| Predicate | characterAlignment |
P22459
|
FINISHED |
| Object | morally ambiguous |
—
|
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: morally ambiguous | Statement: [Mayor Larry Vaughn, characterAlignment, morally ambiguous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterAlignment Context triple: [Mayor Larry Vaughn, characterAlignment, morally ambiguous]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
judgeCharacter
Indicates evaluating or forming an opinion about another entity’s moral qualities, personality, or overall character.
-
C.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
D.
characterArc
Indicates the developmental journey or transformation a character undergoes over the course of a narrative.
-
E.
virtue
Indicates that an entity possesses or exemplifies a morally good quality, trait, or behavior.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3a61b648190b1b6c932e047e161 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29f05f481908814bd11f235e9d0 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.