Triple
T16552426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NUTS classification |
E402103
|
entity |
| Predicate | regionCountVariesBy |
P124030
|
FINISHED |
| Object | member state |
—
|
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: member state | Statement: [NUTS classification, regionCountVariesBy, member state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionCountVariesBy Context triple: [NUTS classification, regionCountVariesBy, member state]
-
A.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
regionNumber
Indicates that an entity is assigned to or associated with a specific numbered region within a larger spatial or organizational division.
-
C.
regionCodeCountApprox
Indicates an approximate count of distinct region codes associated with the given context or dataset.
-
D.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
E.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34fc6735481908b59bbf80fb3469b |
completed | April 18, 2026, 9:32 a.m. |
| PD | Predicate disambiguation | batch_69e296a47b7481909d9958158510c806 |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:15 a.m.