Triple
T879559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onge |
E18994
|
entity |
| Predicate | demographicChallenge |
P21033
|
FINISHED |
| Object | high infant mortality |
—
|
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: high infant mortality | Statement: [Onge, demographicChallenge, high infant mortality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicChallenge Context triple: [Onge, demographicChallenge, high infant mortality]
-
A.
demographicImpact
Indicates how an action, event, or condition affects the size, structure, or composition of a population.
-
B.
demographicPolicy
Indicates a relationship where an authority or organization establishes or applies rules and measures intended to influence the size, structure, or composition of a population.
-
C.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
-
D.
populationIncrease
Indicates that the number of individuals in a population has grown over a specified period of time.
-
E.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
- F. None of above. chosen
Provenance (4 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4acc9d5f4819087afbb75b6ac3dbf |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8d47c081909b02a53e305ccf7a |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab9634948190b25ea1b2e34df87d |
completed | March 1, 2026, 9:11 p.m. |
Created at: March 1, 2026, 7:39 p.m.