Triple
T25205333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FW |
E631234
|
entity |
| Predicate | typicalPolicyArea |
P60745
|
FINISHED |
| Object | education policy in Bavaria |
—
|
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: education policy in Bavaria | Statement: [FW, typicalPolicyArea, education policy in Bavaria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPolicyArea Context triple: [FW, typicalPolicyArea, education policy in Bavaria]
-
A.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
B.
policyAreaScope
chosen
Indicates the specific policy domain or thematic area to which an action, decision, or measure is relevant or applies.
-
C.
influencedPolicyArea
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
-
D.
emphasizesPolicyArea
Indicates that one entity gives particular importance or priority to a specific policy area in its actions, statements, or focus.
-
E.
isPartOfPolicyArea
Indicates that one policy, topic, or issue belongs to, falls under, or is categorized within a broader policy area or domain.
- 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_69e75a8b86c4819089eda22c843b739f |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: April 21, 2026, 12:52 p.m.