Triple
T6361426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Pimlott |
E143117
|
entity |
| Predicate | marriedToOccupationOfSpouse |
P4765
|
FINISHED |
| Object | inventor |
—
|
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: inventor | Statement: [Mary Pimlott, marriedToOccupationOfSpouse, inventor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToOccupationOfSpouse Context triple: [Mary Pimlott, marriedToOccupationOfSpouse, inventor]
-
A.
spouseOccupation
chosen
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
C.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
D.
spouseOfHead
Indicates that one person is the married partner of the individual who holds the position of head (e.g., head of a household, organization, or state).
-
E.
marriedIn
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067fa0d0c819098d01545849142fc |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:32 p.m.