Triple
T29316123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley Lawrence |
E743382
|
entity |
| Predicate | spouseEthnicityOrNationality |
P59312
|
FINISHED |
| Object | Welsh |
—
|
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: Welsh | Statement: [Shirley Lawrence, spouseEthnicityOrNationality, Welsh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseEthnicityOrNationality Context triple: [Shirley Lawrence, spouseEthnicityOrNationality, Welsh]
-
A.
spouseEthnicity
chosen
Indicates the ethnic background or identity of a person’s spouse.
-
B.
spouseCountryOfCitizenship
Indicates the country in which a person's spouse holds legal citizenship.
-
C.
spouseLaterNationality
Indicates that a person’s spouse held or acquired a particular nationality at a later point in time than the reference period.
-
D.
spouseNaturalisationCountry
Indicates the country in which a person’s spouse obtained citizenship through naturalisation.
-
E.
spouseType
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
- 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_69f0912502c8819087d9e8398ee991a8 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: April 28, 2026, 1:20 p.m.