Triple
T32045773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teresa of Portugal |
E818343
|
entity |
| Predicate | causeOfMarriageAnnulment |
P89491
|
FINISHED |
| Object | consanguinity under canon law |
—
|
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: consanguinity under canon law | Statement: [Teresa of Portugal, causeOfMarriageAnnulment, consanguinity under canon law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfMarriageAnnulment Context triple: [Teresa of Portugal, causeOfMarriageAnnulment, consanguinity under canon law]
-
A.
causeOfMarriage
Indicates that one entity is the reason or cause leading to the marriage of another entity or pair of entities.
-
B.
causeOfNonMarriage
chosen
Indicates the reason or factor that prevents or has prevented two entities from being married to each other.
-
C.
struggledToAnnulMarriageWith
Indicates that one party faced significant difficulty or obstacles in legally dissolving or invalidating their marriage with the other party.
-
D.
divorceReason
Indicates the reason or cause for which a marriage ended in divorce between two individuals.
-
E.
allowsDivorce
Indicates that one party permits or grants another party the right or ability to dissolve a marriage or marital union.
- 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_69f348fcfb648190859f6be5e04b7cfe |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 1, 2026, 12:20 a.m.