Triple
T1187430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Direction générale des patrimoines et de l’architecture |
E25278
|
entity |
| Predicate | isPartOfPolicyArea |
P26131
|
FINISHED |
| Object | French cultural 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: French cultural policy | Statement: [Direction générale des patrimoines et de l’architecture, isPartOfPolicyArea, French cultural policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isPartOfPolicyArea Context triple: [Direction générale des patrimoines et de l’architecture, isPartOfPolicyArea, French cultural policy]
-
A.
hasPolicyArea
Indicates that an entity (such as a policy, program, or initiative) is associated with or pertains to a specific policy area or domain.
-
B.
coversPolicyArea
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
-
C.
supportsPolicyArea
Indicates that one entity endorses, advocates for, or is in favor of a particular policy area or domain of public policy.
-
D.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
E.
influencedPolicyArea
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd5578b08190bbe4089857fbf166 |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bd0ab5f88190bb583fc63b4cc150 |
completed | March 1, 2026, 10:26 p.m. |
Created at: March 1, 2026, 7:45 p.m.