Triple
T38703061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biasca |
E950190
|
entity |
| Predicate | demographyRegion |
P7929
|
FINISHED |
| Object | Italian-speaking majority |
—
|
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: Italian-speaking majority | Statement: [Biasca, demographyRegion, Italian-speaking majority]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographyRegion Context triple: [Biasca, demographyRegion, Italian-speaking majority]
-
A.
demographicRegion
chosen
Indicates that an entity is associated with, belongs to, or is characterized by a particular geographic or administrative region for demographic purposes.
-
B.
populationRegion
Indicates that a specified population is located within or associated with a particular geographic region.
-
C.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
-
D.
populationRegionType
Indicates the type or category of region (e.g., city, state, country) to which a given population value or statistic applies.
-
E.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
- 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_69f76f0124408190bb39c3040734846b |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcdfbc71c481908ba7f87907b17782 |
completed | May 7, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe580b8819087f143596b2c79c0 |
completed | May 7, 2026, 6:37 p.m. |
Created at: May 3, 2026, 4:33 p.m.