Triple
T27012655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henriette of Schoenaich-Carolath |
E680434
|
entity |
| Predicate | motherSpouseOrder |
P162029
|
FINISHED |
| Object | second wife |
—
|
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: second wife | Statement: [Henriette of Schoenaich-Carolath, motherSpouseOrder, second wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: motherSpouseOrder Context triple: [Henriette of Schoenaich-Carolath, motherSpouseOrder, second wife]
-
A.
spouseOrder
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
spouseMother
Indicates that one person is the mother of another person’s spouse.
-
C.
spouseOfChildren
Indicates that one entity is the spouse (husband or wife) of the children of another entity.
-
D.
spouseParentage
Indicates that one entity is a parent of the other entity’s spouse.
-
E.
metSpouseThrough
Indicates that one person became acquainted with and subsequently married their spouse as a result of a particular intermediary person, event, place, or context.
- 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_69eeeb53939c8190bd431f32b060f01f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f622abdfac8190988421c946411d7e |
completed | May 2, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69f620e0b37481909a280574decbd443 |
completed | May 2, 2026, 4:05 p.m. |
| PDg | Predicate description generation | batch_69f621c7d3e0819095b1f327637ae4f9 |
completed | May 2, 2026, 4:09 p.m. |
Created at: April 27, 2026, 7:04 a.m.