Triple
T31026667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhang Wenshou |
E790593
|
entity |
| Predicate | spouse's military affiliation |
P135286
|
FINISHED |
| Object | Beiyang Army |
—
|
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: Beiyang Army | Statement: [Zhang Wenshou, spouse's military affiliation, Beiyang Army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouse's military affiliation Context triple: [Zhang Wenshou, spouse's military affiliation, Beiyang Army]
-
A.
spouseServiceBranch
chosen
Indicates the military or service branch in which a person’s spouse serves or has served.
-
B.
militarySpouseOf
Indicates that one person is or was the legally recognized spouse of another person who is serving or has served in the military.
-
C.
spouseOfMilitaryUnit
Indicates that one entity is the spouse or marital partner of a member associated with the specified military unit.
-
D.
spouseCountryOfService
Indicates the country where a person’s spouse is or was serving in an official or professional capacity.
-
E.
hasSpouseMilitaryRank
Indicates that a person’s spouse holds a specific military rank.
- 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_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
Created at: April 29, 2026, 8:58 p.m.