Triple
T25090670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheda-Wiedenbrück |
E628446
|
entity |
| Predicate | twinTownStructure |
P67301
|
FINISHED |
| Object | twin town |
—
|
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: twin town | Statement: [Rheda-Wiedenbrück, twinTownStructure, twin town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: twinTownStructure Context triple: [Rheda-Wiedenbrück, twinTownStructure, twin town]
-
A.
hasTwinCityStructure
chosen
Indicates that one city has an officially recognized twin-city (sister-city) relationship structure with another city.
-
B.
twinTownInCountry
Indicates that a town’s twin or sister city relationship is specifically with a town located in the given country.
-
C.
hasTwinTown
Indicates that two towns or cities are officially paired in a twinning relationship, typically for cultural, social, or economic exchange.
-
D.
hasArchitecturalTwin
Indicates that two entities share nearly identical architectural design, form, or structure, effectively making them architectural counterparts or duplicates.
-
E.
hasTwinStructureWith
Indicates that two entities share an identical or nearly identical structural form, typically as corresponding or mirrored counterparts.
- 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_69e2ff2f58e881908340527bc5d34f07 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f461e9b06c8190a44fd097da84c7cb |
completed | May 1, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69f442c861188190967655c6d8012380 |
completed | May 1, 2026, 6:06 a.m. |
Created at: April 18, 2026, 6:24 a.m.