Triple
T38522281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donna Lee Becht |
E922514
|
entity |
| Predicate | spouseOccupationType |
P4765
|
FINISHED |
| Object | film actor |
—
|
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: film actor | Statement: [Donna Lee Becht, spouseOccupationType, film actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOccupationType Context triple: [Donna Lee Becht, spouseOccupationType, film actor]
-
A.
spouseOccupation
chosen
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
roleInSpouseCareer
Indicates the nature or extent of a person’s involvement or influence in their spouse’s professional career.
-
C.
spouseType
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
-
D.
exSpouseOccupation
Indicates that a person’s former spouse had or has a particular occupation or job role.
-
E.
spouseIndustry
Indicates the industry or sector in which a person's spouse is employed or primarily involved.
- 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_69f76ea5f5588190bd0b28c82e975640 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fed83b1d188190a318b0ad3003200a |
completed | May 9, 2026, 6:46 a.m. |
| PD | Predicate disambiguation | batch_69fed78e03548190b6e6ad93ae8d131d |
completed | May 9, 2026, 6:43 a.m. |
Created at: May 3, 2026, 4:32 p.m.