Triple
T2626139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas's 29th congressional district |
E59121
|
entity |
| Predicate | majorityDemographic |
P21204
|
FINISHED |
| Object | Hispanic |
—
|
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: Hispanic | Statement: [Texas's 29th congressional district, majorityDemographic, Hispanic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorityDemographic Context triple: [Texas's 29th congressional district, majorityDemographic, Hispanic]
-
A.
demographicMajority
chosen
Indicates that one group constitutes more than half of the population within a specified context or area.
-
B.
majorityCountry
Indicates that a given country is the one in which the majority of a specified group, population, or instances are located or occur.
-
C.
majorityType
Indicates that one type or category constitutes more than half of the instances within a given set or context.
-
D.
hasDemographic
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
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_69ab4ac558388190962492cd2e1b0ce6 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abdb0e7b888190bfa5d2e33f00ec0f |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd810d7f481908e81c305772c4c14 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.