Triple
T28323850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke Dao of Jin |
E717352
|
entity |
| Predicate | politicalReforms |
P98268
|
FINISHED |
| Object | restructuring administrative offices |
—
|
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: restructuring administrative offices | Statement: [Duke Dao of Jin, politicalReforms, restructuring administrative offices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: politicalReforms Context triple: [Duke Dao of Jin, politicalReforms, restructuring administrative offices]
-
A.
typeOfReforms
chosen
Indicates the specific kinds or categories of reforms associated with an entity or situation.
-
B.
notableReform
Indicates that an entity is recognized for having initiated, led, or been central to a significant reform or transformative change in a system, policy, or institution.
-
C.
goalOfReforms
Indicates that a reform or set of reforms is undertaken with the aim or intended objective of achieving a particular outcome.
-
D.
advocatedReformOf
Indicates that one entity publicly supported or promoted changes to another entity, typically aiming to improve or modify its structure, policies, or practices.
-
E.
reform
Indicates bringing about significant changes to an existing system, practice, or entity in order to improve or correct it.
- 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_69eff6e6c3b08190ad78de6ba7f04548 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f7516d5b4081908588a6feb541f355 |
completed | May 3, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69f74d40ebb081909daf60623e38f41d |
completed | May 3, 2026, 1:27 p.m. |
Created at: April 28, 2026, 12:26 a.m.