Triple
T29897726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US–China Business Council |
E759321
|
entity |
| Predicate | topicOfExpertise |
P140458
|
FINISHED |
| Object | Chinese market conditions |
—
|
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: Chinese market conditions | Statement: [US–China Business Council, topicOfExpertise, Chinese market conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topicOfExpertise Context triple: [US–China Business Council, topicOfExpertise, Chinese market conditions]
-
A.
subjectSpecialization
chosen
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
B.
disciplinaryFocus
Indicates the primary academic or professional field, subject area, or discipline that something is centered on or concerned with.
-
C.
researchTopic
Indicates that a subject conducts or focuses research on a particular topic or area of study.
-
D.
subjectCategories
Indicates that an entity is associated with one or more subject-based categories or classifications.
-
E.
regionOfStudy
Indicates the academic or research area that is the focus of someone’s study or investigation.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6772c7cc48190b2a3616b9fdcb7e4 |
completed | May 2, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:05 p.m.