Triple
T24500395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | von Galen family |
E617915
|
entity |
| Predicate | hasTraditionalSeatRegion |
P79532
|
FINISHED |
| Object | Münster region |
—
|
NE NERFINISHED |
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: Münster region | Statement: [von Galen family, hasTraditionalSeatRegion, Münster region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalSeatRegion Context triple: [von Galen family, hasTraditionalSeatRegion, Münster region]
-
A.
locatedInTraditionalRegion
Indicates that an entity is situated within a specific traditional or historically recognized region.
-
B.
hasTraditionalTerritoryType
Indicates that an entity’s traditional territory is classified as belonging to a specific type or category of territory.
-
C.
hasTraditionalArea
chosen
Indicates that an entity is associated with or belongs to a customary or historically recognized geographic area.
-
D.
hasTraditionalLanguageRegion
Indicates the geographic region traditionally associated with the use or origin of a particular language.
-
E.
hasTraditionalCountryNear
Indicates that one entity has a traditional or culturally recognized country located geographically close to it.
- 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_69e2d7f682108190a1a7ca5fd485ee8a |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:23 a.m.