Triple
T34989969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Y’ffre |
E1009350
|
entity |
| Predicate | alignmentInLore |
P120754
|
FINISHED |
| Object | generally benevolent |
—
|
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: generally benevolent | Statement: [Y’ffre, alignmentInLore, generally benevolent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alignmentInLore Context triple: [Y’ffre, alignmentInLore, generally benevolent]
-
A.
alignmentInStory
Indicates how a character’s moral or ethical stance (e.g., good, neutral, evil) is portrayed within the context of a specific story.
-
B.
hasAlignmentInFiction
chosen
Indicates that a fictional character, group, or entity possesses a specific moral or ethical alignment within a fictional setting.
-
C.
alignedAgainst
Indicates that two or more entities are united in opposition to a common target, side, or objective.
-
D.
alignmentAtIntroduction
Indicates that two entities share a particular alignment or stance at the moment one is first introduced.
-
E.
eraAlignment
Indicates that two entities are associated with, or correspond to, the same historical or temporal era.
- 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_69f76dca50dc8190b71f39defe186be8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.