Triple
T3481290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarice Orsini |
E73494
|
entity |
| Predicate | spouseMarriageType |
P8399
|
FINISHED |
| Object | arranged marriage |
—
|
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: arranged marriage | Statement: [Clarice Orsini, spouseMarriageType, arranged marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseMarriageType Context triple: [Clarice Orsini, spouseMarriageType, arranged marriage]
-
A.
marriageType
chosen
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
B.
spouseType
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
-
C.
spouseStatusAtMarriage
Indicates the marital status each partner held at the time their marriage to one another was formed.
-
D.
spouseStatus
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
E.
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.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb75850c8190ad02cf2bde8be8a7 |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.