Triple
T31026666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhang Wenshou |
E790593
|
entity |
| Predicate | spouse's country of citizenship |
P4766
|
FINISHED |
| Object | China |
—
|
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: China | Statement: [Zhang Wenshou, spouse's country of citizenship, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouse's country of citizenship Context triple: [Zhang Wenshou, spouse's country of citizenship, China]
-
A.
spouseCountryOfCitizenship
chosen
Indicates the country in which a person's spouse holds legal citizenship.
-
B.
spouseLaterNationality
Indicates that a person’s spouse held or acquired a particular nationality at a later point in time than the reference period.
-
C.
spouseNaturalisationCountry
Indicates the country in which a person’s spouse obtained citizenship through naturalisation.
-
D.
spouseCountryOfService
Indicates the country where a person’s spouse is or was serving in an official or professional capacity.
-
E.
spouseOfOfficeHolderJurisdiction
Indicates that one person is the spouse of a public office holder, with the relationship specifically tied to the jurisdiction in which that office is held.
- 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_69f224c811508190a7de096a5b1f5798 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
Created at: April 29, 2026, 8:58 p.m.