Triple
T17070105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hua Zhou |
E414192
|
entity |
| Predicate | relationshipTypeWithHuaMulan |
P10690
|
FINISHED |
| Object | supportive but bound by tradition |
—
|
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: supportive but bound by tradition | Statement: [Hua Zhou, relationshipTypeWithHuaMulan, supportive but bound by tradition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithHuaMulan Context triple: [Hua Zhou, relationshipTypeWithHuaMulan, supportive but bound by tradition]
-
A.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
B.
relationshipTypeWith Suzie Wong
Indicates the specific nature or category of relationship that an entity has with Suzie Wong.
-
C.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipTypeWithEunice
Indicates the specific nature or category of the relationship that an entity has with Eunice.
-
E.
familyRelationToEmperor
Indicates that one entity is related to an emperor by family ties, specifying a kinship or familial connection between them.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbbfb1f08190807301ff6e573cf5 |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d642f74819098c014135e249b27 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:34 a.m.