Triple
T25704511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Booby |
E644549
|
entity |
| Predicate | hasVirtueContrastWith |
P117295
|
FINISHED |
| Object | Joseph Andrews |
—
|
NE NERFINISHED |
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: Joseph Andrews | Statement: [Lady Booby, hasVirtueContrastWith, Joseph Andrews]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVirtueContrastWith Context triple: [Lady Booby, hasVirtueContrastWith, Joseph Andrews]
-
A.
vowedVirtue
Indicates that an entity has formally promised or committed to uphold a particular virtue or moral quality.
-
B.
providesContrastWith
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
C.
virtuePraisedFor
Indicates that a particular virtue is being commended, admired, or spoken of approvingly by someone.
-
D.
isMoralFoilFor
chosen
Indicates that one entity serves as a contrasting counterpart whose differing moral qualities highlight or emphasize the moral traits of another entity.
-
E.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to 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_69e77e83c8ec8190bf52fcdac4838984 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f621fcea1481909b6f8b3af1ee6820 |
completed | May 2, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69f620dc38088190b56b2b15ed75b3c2 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 21, 2026, 9:01 p.m.