Triple
T14242324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viscountess Beaconsfield |
E353040
|
entity |
| Predicate | titleHolderSpouseOccupation |
P4765
|
FINISHED |
| Object | Prime Minister of the United Kingdom |
—
|
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: Prime Minister of the United Kingdom | Statement: [Viscountess Beaconsfield, titleHolderSpouseOccupation, Prime Minister of the United Kingdom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleHolderSpouseOccupation Context triple: [Viscountess Beaconsfield, titleHolderSpouseOccupation, Prime Minister of the United Kingdom]
-
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.
spousePlaceOfWork
Indicates that the place of work specified belongs to the spouse of the referenced person.
-
D.
spouseIndustry
Indicates the industry or sector in which a person's spouse is employed or primarily involved.
-
E.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6244ad188190b9d9db7914240410 |
completed | April 14, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69de05bf069c8190b69f00f00f5eb126 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:08 a.m.