Triple
T23442626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H. Jon Benjamin as Bob Belcher |
E565443
|
entity |
| Predicate | characterSpouse |
P138299
|
FINISHED |
| Object | Linda Belcher |
—
|
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: Linda Belcher | Statement: [H. Jon Benjamin as Bob Belcher, characterSpouse, Linda Belcher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterSpouse Context triple: [H. Jon Benjamin as Bob Belcher, characterSpouse, Linda Belcher]
-
A.
spouseCharacterOf
chosen
Indicates a marital relationship where one character is the spouse of another character.
-
B.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
C.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
D.
spouseOfWork
Indicates that one person is the spouse of another specifically in the context of their workplace or professional environment.
-
E.
spouseAlsoKnownAs
Indicates that a person’s spouse is referred to by an alternative name or alias.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.