Triple
T32777740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress Xiaokangzhang |
E838264
|
entity |
| Predicate | appointedEmpressConsortBy |
P188904
|
FINISHED |
| Object | Shunzhi Emperor |
—
|
NE NERFINISHED |
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: Shunzhi Emperor | Statement: [Empress Xiaokangzhang, appointedEmpressConsortBy, Shunzhi Emperor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appointedEmpressConsortBy Context triple: [Empress Xiaokangzhang, appointedEmpressConsortBy, Shunzhi Emperor]
-
A.
appointedEmpressBy
chosen
Indicates that one entity is made empress through the formal decision or action of another entity.
-
B.
associatedEmpress
Indicates a relationship where an empress is linked or connected to another entity, such as a person, place, event, or object, in a relevant or context-specific way.
-
C.
eraAsEmpress
Indicates the time period during which a person held the role or status of empress.
-
D.
realmAsEmpress
Indicates that an entity holds the position or role of empress over a specified realm or domain.
-
E.
reignAsEmpress
Indicates that a person holds and exercises the supreme imperial authority in the role of empress over a realm or empire.
- 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_69f3493a824c8190938489ba69041d08 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 1, 2026, 1:13 a.m.