Triple
T31026665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhang Wenshou |
E790593
|
entity |
| Predicate | spouse's position held |
P4763
|
FINISHED |
| Object | President of the Beiyang Government |
—
|
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: President of the Beiyang Government | Statement: [Zhang Wenshou, spouse's position held, President of the Beiyang Government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouse's position held Context triple: [Zhang Wenshou, spouse's position held, President of the Beiyang Government]
-
A.
positionHeldBySpouse
chosen
Indicates that a particular position, role, or office is or was held by the spouse of a given person.
-
B.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
C.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
D.
hasSpousePositionInFamily
Indicates that a person’s spouse holds a specific role or position within the family structure.
-
E.
roleDuringSpouseTenure
Indicates that a person held a particular role or position specifically during the period when their spouse was in office or serving in a defined tenure.
- 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_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
Created at: April 29, 2026, 8:58 p.m.