Triple
T13611810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bessie Springs Smith |
E325206
|
entity |
| Predicate | marriedToProfessionOfSpouse |
P4765
|
FINISHED |
| Object | architect |
—
|
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: architect | Statement: [Bessie Springs Smith, marriedToProfessionOfSpouse, architect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToProfessionOfSpouse Context triple: [Bessie Springs Smith, marriedToProfessionOfSpouse, architect]
-
A.
roleInSpouseCareer
Indicates the nature or extent of a person’s involvement or influence in their spouse’s professional career.
-
B.
spouseOccupation
chosen
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
C.
spouseIndustry
Indicates the industry or sector in which a person's spouse is employed or primarily involved.
-
D.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
E.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:50 p.m.