Triple
T849878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elisabeth Pepys |
E18359
|
entity |
| Predicate | marriageCharacterization |
P21095
|
FINISHED |
| Object | often turbulent |
—
|
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: often turbulent | Statement: [Elisabeth Pepys, marriageCharacterization, often turbulent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageCharacterization Context triple: [Elisabeth Pepys, marriageCharacterization, often turbulent]
-
A.
marriagePattern
Indicates the typical form or structure of a marriage relationship, such as how partners are selected, organized, or related within a social or cultural system.
-
B.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
C.
maritalBasis
Indicates that the relationship or status in question is founded on, justified by, or determined due to a marital relationship between the involved entities.
-
D.
houseByMarriage
Indicates a familial or household relationship established through marriage rather than by blood or direct residence.
-
E.
marriedInto
Indicates that one entity became connected to another’s family or group through marriage, rather than by birth or prior membership.
- F. None of above. chosen
Provenance (4 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac215194819099e6bc1b5df58fb3 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa807adc8190ad808a573cf8e923 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4abb157d08190a7d7281eb3f1b788 |
completed | March 1, 2026, 9:12 p.m. |
Created at: March 1, 2026, 7:38 p.m.