Triple
T31799668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jumelage with Ameland |
E811695
|
entity |
| Predicate | partnerEntityType |
P178348
|
FINISHED |
| Object | German city |
—
|
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: German city | Statement: [Jumelage with Ameland, partnerEntityType, German city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partnerEntityType Context triple: [Jumelage with Ameland, partnerEntityType, German city]
-
A.
businessEntity
Indicates a relationship where an entity functions as or is classified as a business or commercial organization.
-
B.
businessPartner
Indicates a formal collaborative relationship between two entities that work together in a business context, typically sharing responsibilities, risks, or benefits.
-
C.
employerOrPartner
Indicates that one entity is either the employer of, or a business partner with, another entity.
-
D.
partnerStructure
Indicates a relationship where one entity is organized, configured, or structured in coordination with, or as a counterpart to, another entity in a partnership context.
-
E.
partnerInOrganizationWith
Indicates that two entities are associated as partners within the same organization or organizational context.
- 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_69f348e70d188190b4637c5509f81274 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f70e8755a48190931eaa77946f9460 |
completed | May 3, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f70abc00848190a1c3f495ef6c8dc6 |
completed | May 3, 2026, 8:43 a.m. |
| PDg | Predicate description generation | batch_69f70e854b9c8190a3416e2189e17742 |
completed | May 3, 2026, 8:59 a.m. |
Created at: April 30, 2026, 11:41 p.m.