Triple
T34648490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MFF |
E889768
|
entity |
| Predicate | policyAreasCovered |
P60745
|
FINISHED |
| Object | cohesion 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: cohesion policy | Statement: [MFF, policyAreasCovered, cohesion policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyAreasCovered Context triple: [MFF, policyAreasCovered, cohesion policy]
-
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.
policySubcategories
Indicates that a given policy is associated with, or broken down into, one or more more specific subcategory policies.
-
D.
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.
-
E.
policyCategory
Indicates the classification or type of policy under which an item, rule, or action is grouped.
- 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_69f349d825c88190bfc6170ac9281260 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 1, 2026, 2:04 a.m.