Triple
T6791829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady of the Republic of the China |
E155948
|
entity |
| Predicate | mayAdvocateFor |
P33
|
FINISHED |
| Object | education |
—
|
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: education | Statement: [First Lady of the Republic of the China, mayAdvocateFor, education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayAdvocateFor Context triple: [First Lady of the Republic of the China, mayAdvocateFor, education]
-
A.
advocates
chosen
Indicates that one entity publicly supports, recommends, or argues in favor of another entity or its interests.
-
B.
advocatesAgainst
Indicates that one entity actively opposes, argues against, or campaigns to prevent or stop another entity, action, or idea.
-
C.
hasAdvocacyMethod
Indicates the method, strategy, or approach used to advocate for a cause, issue, or entity.
-
D.
mayEngageIn
Indicates that one entity is permitted or authorized to participate in or perform a particular activity or interaction with another entity.
-
E.
mayActThrough
Indicates that an entity can exert influence, perform an action, or have an effect by means of another entity, mechanism, or intermediary.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2ae4d1c819089ac6b3abf11a341 |
completed | March 27, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d0979ce0819094678896da4e3169 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:15 p.m.