Triple
T14255253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China – Measures Affecting Imports of Automobile Parts |
E353367
|
entity |
| Predicate | relatedToSector |
P47145
|
FINISHED |
| Object | automotive industry |
—
|
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: automotive industry | Statement: [China – Measures Affecting Imports of Automobile Parts, relatedToSector, automotive industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToSector Context triple: [China – Measures Affecting Imports of Automobile Parts, relatedToSector, automotive industry]
-
A.
associatedWithEconomicSector
chosen
Indicates that an entity has a connection or involvement with a particular economic sector, such as operating, participating, or being relevant within that sector.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
relationToIndustry
Indicates how an entity is connected or relevant to a particular industry, such as through involvement, impact, or association.
-
D.
targetsSector
Indicates that an entity is directed toward, focused on, or intended to affect a particular economic or industry sector.
-
E.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62992a188190bc046fbab5a149d6 |
completed | April 14, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:09 a.m.