Triple
T32082539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Asian languages and cultures |
E819337
|
entity |
| Predicate | typicalRegionOfStudy |
P85474
|
FINISHED |
| Object | China |
E5561
|
NE 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: China | Statement: [East Asian languages and cultures, typicalRegionOfStudy, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRegionOfStudy Context triple: [East Asian languages and cultures, typicalRegionOfStudy, China]
-
A.
primaryRegionOfStudy
Indicates the main geographic or topical region that is the focus of an entity’s academic or research study.
-
B.
regionOfStudy
chosen
Indicates the academic or research area that is the focus of someone’s study or investigation.
-
C.
jurisdictionOfStudy
Indicates the legal or geographic jurisdiction within which a given study is conducted or governed.
-
D.
regionOfAcademicInterest
Indicates that an entity has a particular academic field or subject area as its focus of interest or study.
-
E.
regionOfAcademicFocus
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
- F. None of above.
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_69f348ff8ef88190931c08ba530a36bc |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2eddc563188190a63e917e15c11cd2 |
completed | June 14, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:24 a.m.