Triple
T4117871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Page Act of 1875 |
E90337
|
entity |
| Predicate | disproportionateImpactOn |
P51941
|
FINISHED |
| Object | Chinese women |
—
|
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: Chinese women | Statement: [Page Act of 1875, disproportionateImpactOn, Chinese women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disproportionateImpactOn Context triple: [Page Act of 1875, disproportionateImpactOn, Chinese women]
-
A.
majorImpact
Indicates that one entity has a significant, highly influential, or transformative effect on another entity or outcome.
-
B.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
C.
discriminatedAgainst
Indicates that one entity treats another unfairly or unequally based on a particular characteristic, such as race, gender, or other protected attributes.
-
D.
hadRepresentationDisproportionateToPopulation
chosen
Indicates that the representation of a group or entity was not proportional to its share of the overall population.
-
E.
examinesImpactOn
Indicates that one entity studies, evaluates, or analyzes the effects or consequences that another entity has on a specified subject or outcome.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0246e40081908ad6741a830ca68e |
completed | March 9, 2026, 5:24 p.m. |
| PD | Predicate disambiguation | batch_69af01867698819098e4144634b2ec4f |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:41 p.m.