Triple
T35942735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Spacely |
E1039496
|
entity |
| Predicate | spouseOfEmployerOf |
P196920
|
FINISHED |
| Object | George Jetson |
—
|
NE NERFINISHED |
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: George Jetson | Statement: [Mrs. Spacely, spouseOfEmployerOf, George Jetson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOfEmployerOf Context triple: [Mrs. Spacely, spouseOfEmployerOf, George Jetson]
-
A.
spouseOfWork
Indicates that one person is the spouse of another specifically in the context of their workplace or professional environment.
-
B.
spouseWorkWith
Indicates that a person’s spouse works together with a specified person, typically as colleagues in the same workplace or professional context.
-
C.
spousePlaceOfWork
Indicates that the place of work specified belongs to the spouse of the referenced person.
-
D.
spouseMember
Indicates that one entity is the spouse (married partner) of another entity.
-
E.
spouseInFamily
Indicates that a person is a spouse (married partner) within the context of a specific family unit.
- 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_69f76e24bbd0819096b837d35371639a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe6e492bf8819080b25221d13445ea |
completed | May 8, 2026, 11:14 p.m. |
| PD | Predicate disambiguation | batch_69fe6dd33a6881908fe9bbbc184cab51 |
completed | May 8, 2026, 11:12 p.m. |
| PDg | Predicate description generation | batch_69fe6e4801108190a6be6bec2d52a1b9 |
completed | May 8, 2026, 11:14 p.m. |
Created at: May 3, 2026, 4:07 p.m.