Triple
T25441192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Udharbond Assembly constituency |
E637510
|
entity |
| Predicate | hasLanguageDemographics |
P61052
|
FINISHED |
| Object | Bengali-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: Bengali-speaking majority | Statement: [Udharbond Assembly constituency, hasLanguageDemographics, Bengali-speaking majority]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageDemographics Context triple: [Udharbond Assembly constituency, hasLanguageDemographics, Bengali-speaking majority]
-
A.
demographicsSignificantLanguage
chosen
Indicates that a particular language is significantly represented or prevalent within the demographic profile of a population or group.
-
B.
hasDemographic
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
C.
demographicsIn
Indicates that demographic information is associated with or applies within a specified geographic or organizational area.
-
D.
demographicsDataAvailable
Indicates that demographic information about a population or group is present, accessible, and ready for use or analysis.
-
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_69e75db6c97081908178383fa632b193 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 21, 2026, 2 p.m.