Triple
T28632274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borealis museum |
E724674
|
entity |
| Predicate | hasIndustryTheme |
P122227
|
FINISHED |
| Object | pulp and paper |
—
|
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: pulp and paper | Statement: [Borealis museum, hasIndustryTheme, pulp and paper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustryTheme Context triple: [Borealis museum, hasIndustryTheme, pulp and paper]
-
A.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
B.
hasIndustrySection
chosen
Indicates that an entity belongs to, is categorized under, or is associated with a particular industry section.
-
C.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
D.
hasInvestmentTheme
Indicates that an investment, fund, or financial product is associated with a particular overarching theme or strategic focus (such as technology, sustainability, or healthcare).
-
E.
hasOccupationTheme
Indicates that something (such as a work or resource) centrally involves or focuses on a particular occupation or type of work as its main theme.
- 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_69f01d8328c48190bc0e5f9b9b848582 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fddd373cdc8190be1b12e70e4deb1f |
completed | May 8, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69fddc6915a88190ad41e379aa3ede13 |
completed | May 8, 2026, 12:51 p.m. |
Created at: April 28, 2026, 4:37 a.m.