Triple
T16317561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jefferson D'Arcy |
E396209
|
entity |
| Predicate | maritalOrderForSpouse |
P4764
|
FINISHED |
| Object | Marcy's second husband |
—
|
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: Marcy's second husband | Statement: [Jefferson D'Arcy, maritalOrderForSpouse, Marcy's second husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maritalOrderForSpouse Context triple: [Jefferson D'Arcy, maritalOrderForSpouse, Marcy's second husband]
-
A.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
governedThroughSpouse
Indicates that one entity exercised governing authority or political power indirectly through their spouse, who held the formal ruling position.
-
C.
spouseAppointedBy
Indicates that a person’s spouse was selected or assigned to a role or position by the referenced appointing entity.
-
D.
granteeSpouse
Indicates that one person is the spouse of the person who receives a grant, transfer, or similar benefit.
-
E.
throughSpouse
Indicates a relationship or connection that exists indirectly between two entities by way of one entity’s spouse.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b31b988190bb1fde36dae11bbd |
completed | April 17, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69e219fc72c881909d452274e7af8238 |
completed | April 17, 2026, 11:31 a.m. |
Created at: April 10, 2026, 5:06 a.m.