Triple
T31443751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Speak Mandarin Campaign |
E802135
|
entity |
| Predicate | governmentPolicyType |
P148602
|
FINISHED |
| Object | language policy |
—
|
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: language policy | Statement: [Speak Mandarin Campaign, governmentPolicyType, language policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: governmentPolicyType Context triple: [Speak Mandarin Campaign, governmentPolicyType, language policy]
-
A.
policyTopic
chosen
Indicates that one entity is about, concerned with, or categorized under a particular policy-related subject or theme.
-
B.
coordinatesNationalPoliciesFor
Indicates that one entity organizes and harmonizes national-level policies or strategies across multiple actors or domains.
-
C.
economicPolicyType
Indicates the classification of an economic policy according to its general type or category (e.g., fiscal, monetary, trade).
-
D.
nationalPolicy
Indicates that an entity establishes, embodies, or is governed by an official policy at the level of a nation-state.
-
E.
policyFocus
Indicates that an entity (such as a person, organization, or document) is primarily concerned with, directed toward, or centered on a particular policy area or issue.
- 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_69f348c5a6bc819092a557e95438976f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ffde9263248190996f970b6cf6e49d |
completed | May 10, 2026, 1:25 a.m. |
| PD | Predicate disambiguation | batch_69ffdd760f1c8190abc6c0c1cd97ba5f |
completed | May 10, 2026, 1:20 a.m. |
Created at: April 30, 2026, 9:07 p.m.