Triple
T31026668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhang Wenshou |
E790593
|
entity |
| Predicate | spouse's political era |
P100327
|
FINISHED |
| Object | early Republic of China |
—
|
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: early Republic of China | Statement: [Zhang Wenshou, spouse's political era, early Republic of China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouse's political era Context triple: [Zhang Wenshou, spouse's political era, early Republic of China]
-
A.
spouseEra
chosen
Indicates that two individuals are spouses during a specified historical period or era.
-
B.
spousePoliticalContext
Indicates that there is a political or politically relevant relationship, role, or context involving a person’s spouse in connection with the subject entity.
-
C.
spousePoliticalAlignment
Indicates that two individuals are spouses and specifies the political alignment or affiliation associated with that spousal relationship.
-
D.
roleDuringHusbandPresidency
Indicates the role or position a person held specifically during her husband's term as president.
-
E.
spouseOfFormer
Indicates that one entity is the spouse of another entity who is a former holder of some role, status, or position.
- 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_69fd231cab588190ad0953dc8f4af8f2 |
completed | May 7, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69fd1aa3f1c481909fe6e9cab1383551 |
completed | May 7, 2026, 11:05 p.m. |
Created at: April 29, 2026, 8:58 p.m.