Triple
T19579424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | María del Pilar Teresa Cayetana de Silva Álvarez de Toledo |
E489946
|
entity |
| Predicate | hasNoSurvivingIssue |
P124078
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [María del Pilar Teresa Cayetana de Silva Álvarez de Toledo, hasNoSurvivingIssue, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoSurvivingIssue Context triple: [María del Pilar Teresa Cayetana de Silva Álvarez de Toledo, hasNoSurvivingIssue, true]
-
A.
hasNoSurvivingLegitimateIssue
chosen
Indicates that a person has no living children or descendants who are legally recognized as legitimate heirs.
-
B.
hasNoIssue
Indicates that there are no problems, defects, or conflicts associated with the referenced entity or situation.
-
C.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
D.
hasTargetIssue
Indicates that an entity is associated with or directed toward a specific issue, problem, or concern as its focus.
-
E.
containsIssue
Indicates that one entity includes, encompasses, or has within it a particular issue, problem, or defect.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402763b8819099e535979f094f9d |
completed | April 20, 2026, 3:03 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:42 p.m.