Triple
T9579904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canterbury Association |
E231142
|
entity |
| Predicate | demographicTarget |
P17586
|
FINISHED |
| Object | English middle-class settlers |
—
|
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: English middle-class settlers | Statement: [Canterbury Association, demographicTarget, English middle-class settlers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicTarget Context triple: [Canterbury Association, demographicTarget, English middle-class settlers]
-
A.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
B.
targetedPopulation
chosen
Indicates the group of individuals or entities that an action, intervention, or effect is specifically directed toward.
-
C.
hasDemographic
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
D.
demographicsDescriptor
Indicates a descriptive attribute or classification that characterizes the demographic properties of an entity or group.
-
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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99ca96a48190a6fbe88387be6883 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.