Triple
T10028629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yongzheng |
E204794
|
entity |
| Predicate | administrativeFocus |
P32450
|
FINISHED |
| Object | anti-corruption measures |
—
|
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: anti-corruption measures | Statement: [Yongzheng, administrativeFocus, anti-corruption measures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeFocus Context triple: [Yongzheng, administrativeFocus, anti-corruption measures]
-
A.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
B.
administrativeFeature
chosen
Indicates that one entity serves as an administrative or governance-related feature, function, or attribute associated with another entity.
-
C.
organizationFocus
Indicates the primary area of activity, mission, or specialization that an organization is oriented toward or concentrated on.
-
D.
focusOf
Indicates that one entity is the primary subject, target, or center of attention, activity, or interest for another entity.
-
E.
formerFocus
Indicates that an entity previously served as the primary focus or main subject of attention, but no longer holds that status.
- 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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcde51c408190afb34010b1707014 |
completed | April 2, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:54 p.m.