Triple
T15396863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catalina de Aragón |
E368197
|
entity |
| Predicate | representadaEn |
P90996
|
FINISHED |
| Object | literatura |
—
|
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: literatura | Statement: [Catalina de Aragón, representadaEn, literatura]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: representadaEn Context triple: [Catalina de Aragón, representadaEn, literatura]
-
A.
representationIn
chosen
Indicates that one entity serves as a depiction, model, or stand-in for another entity within a given context or medium.
-
B.
representedTo
Indicates that one entity formally acted on behalf of or served as the representative of another entity in a given context or interaction.
-
C.
representedFor
Indicates that one entity has acted or served as the official representative or proxy on behalf of another entity.
-
D.
hasRepresentationIn
Indicates that one entity is represented, depicted, or encoded within another entity, such as a concept, object, or data structure having a corresponding representation in a specific medium or context.
-
E.
areRepresentedBy
Indicates that one entity serves as a representation, proxy, or stand-in for 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8c5d40819086622b70edcb6294 |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27b8cac8190bfa77698d53c5d1c |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:19 a.m.