Triple
T36660535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lianghu Academy |
E905109
|
entity |
| Predicate | educationalReformContext |
P98268
|
FINISHED |
| Object | late Qing reforms |
—
|
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: late Qing reforms | Statement: [Lianghu Academy, educationalReformContext, late Qing reforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationalReformContext Context triple: [Lianghu Academy, educationalReformContext, late Qing reforms]
-
A.
educationalContext
Indicates the situational or institutional setting in which an educational activity, interaction, or resource takes place.
-
B.
educationPolicy
Indicates a relationship where an authority or entity establishes, governs, or influences rules, strategies, or frameworks guiding an education system or educational practices.
-
C.
relatedReforms
Indicates that one reform is connected or associated with another reform, typically through shared goals, content, or impact.
-
D.
typeOfReforms
chosen
Indicates the specific kinds or categories of reforms associated with an entity or situation.
-
E.
educationalImpact
Indicates the effect or influence that one entity has on the learning, knowledge, or educational outcomes of another.
- 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_69f76e6e3b908190970251b30f76ad71 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c77c19948190a856ebf393846c98 |
completed | May 3, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69f7c4796ebc819084a0dc08505e5f14 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.