Triple
T25507883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duessa |
E639293
|
entity |
| Predicate | nationalityInAllegory |
P15237
|
FINISHED |
| Object | Spanish (in anti-Catholic reading) |
—
|
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: Spanish (in anti-Catholic reading) | Statement: [Duessa, nationalityInAllegory, Spanish (in anti-Catholic reading)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalityInAllegory Context triple: [Duessa, nationalityInAllegory, Spanish (in anti-Catholic reading)]
-
A.
nationalityInMyth
Indicates that a mythological figure, character, or entity is associated with a particular nationality or cultural tradition within mythology.
-
B.
nationalityInStory
chosen
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
C.
nationalityTheme
Indicates that an entity is thematically associated with, or centrally involves, a particular nationality or country.
-
D.
nationalityInText
Indicates that a person's nationality is mentioned or specified within a given text.
-
E.
depictsNationality
Indicates that one entity visually represents or portrays the nationality or national identity of 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f65aa07c048190a5df30d53d8f0cf5 |
completed | May 2, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 21, 2026, 2:47 p.m.