Triple
T15396860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catalina de Aragón |
E368197
|
entity |
| Predicate | títuloPosteriorEnInglaterra |
P28033
|
FINISHED |
| Object | princesa viuda de Gales |
—
|
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: princesa viuda de Gales | Statement: [Catalina de Aragón, títuloPosteriorEnInglaterra, princesa viuda de Gales]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: títuloPosteriorEnInglaterra Context triple: [Catalina de Aragón, títuloPosteriorEnInglaterra, princesa viuda de Gales]
-
A.
equivalentTitleInEngland
chosen
Indicates that one title corresponds to an equivalent or matching title within the context of England’s system of titles.
-
B.
equivalentTitleInUnitedKingdom
Indicates that one entity has a title that is considered the equivalent of another entity’s title specifically within the context of the United Kingdom.
-
C.
titleInEnglish
Indicates that an entity’s title or name is given in the English language.
-
D.
returnedToEngland
Indicates that an entity went back to England after having been away.
-
E.
successorInUK
Indicates that one entity directly follows another in a succession specific to the United Kingdom, such as in an official role, title, or office.
- 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.