Triple
T30760405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emperor He of Han |
E783217
|
entity |
| Predicate | empress |
P122008
|
FINISHED |
| Object | Empress Deng Sui |
—
|
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: Empress Deng Sui | Statement: [Emperor He of Han, empress, Empress Deng Sui]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: empress Context triple: [Emperor He of Han, empress, Empress Deng Sui]
-
A.
eraAsEmpress
Indicates the time period during which a person held the role or status of empress.
-
B.
reignAsEmpress
Indicates that a person holds and exercises the supreme imperial authority in the role of empress over a realm or empire.
-
C.
realmAsEmpress
Indicates that an entity holds the position or role of empress over a specified realm or domain.
-
D.
associatedEmpress
chosen
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.
-
E.
motherOfEmperor
Indicates that one entity is the mother (biological or adoptive) of another entity who holds the title or role of emperor.
- 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_69f224b047f48190b4f5efeb7ee97b37 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68f9b56988190a95f2706bb6b3217 |
completed | May 2, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:39 p.m.