Triple
T31472284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CNBC Asia Business Leader Award |
E802890
|
entity |
| Predicate | hasIndustryScope |
P53279
|
FINISHED |
| Object | multiple industries |
—
|
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: multiple industries | Statement: [CNBC Asia Business Leader Award, hasIndustryScope, multiple industries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustryScope Context triple: [CNBC Asia Business Leader Award, hasIndustryScope, multiple industries]
-
A.
containsIndustry
chosen
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
B.
hasIndustrySection
Indicates that an entity belongs to, is categorized under, or is associated with a particular industry section.
-
C.
isPartOfIndustry
Indicates that one entity belongs to, operates within, or is categorized under a particular industry sector.
-
D.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
E.
hasCareerScope
Indicates that an entity’s career, role, or profession extends over, is relevant to, or is defined within a particular domain, field, or scope.
- 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_69f348c84c1c81908739f100ecf7394e |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff49016dcc8190a8a43868c728b4f1 |
completed | May 9, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69ff4891924c8190b5be340e2520e012 |
completed | May 9, 2026, 2:45 p.m. |
Created at: April 30, 2026, 9:26 p.m.