Triple
T3548002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chenghua Emperor |
E75041
|
entity |
| Predicate | courtCorruption |
P50506
|
FINISHED |
| Object | increased during later years of reign |
—
|
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: increased during later years of reign | Statement: [Chenghua Emperor, courtCorruption, increased during later years of reign]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courtCorruption Context triple: [Chenghua Emperor, courtCorruption, increased during later years of reign]
-
A.
courtOfficial
Indicates that an entity serves in an official capacity within a court, holding an authorized role in judicial or legal proceedings.
-
B.
affectedCourt
Indicates that a particular court is impacted or influenced by a specified action, decision, or legal matter.
-
C.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
D.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
E.
courtContext
Indicates the legal or judicial setting, circumstances, or framework within which a court-related action or relationship takes place.
- F. None of above. chosen
Provenance (4 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_69ad85d33c6c819081d5ac1df13b5680 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbfd0eb6081908f1380db4cfade87 |
completed | March 8, 2026, 6:28 p.m. |
| PD | Predicate disambiguation | batch_69adb83270ac819083967db0570167d2 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adba25c66c81909a05a97327828c41 |
completed | March 8, 2026, 6:04 p.m. |
Created at: March 8, 2026, 3:20 p.m.