Triple
T2014520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lübeck Katharineum |
E43763
|
entity |
| Predicate | hasSchoolBuildingFeature |
P6684
|
FINISHED |
| Object | historic cloister |
—
|
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: historic cloister | Statement: [Lübeck Katharineum, hasSchoolBuildingFeature, historic cloister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSchoolBuildingFeature Context triple: [Lübeck Katharineum, hasSchoolBuildingFeature, historic cloister]
-
A.
hasCampusFeature
Indicates that a campus possesses or includes a specific physical or functional feature.
-
B.
hasArchitecturalFeature
chosen
Indicates that one entity possesses, includes, or is characterized by a specific architectural feature or element.
-
C.
containsBuilding
Indicates that one location or area includes a building within its boundaries.
-
D.
hasFictionalSchool
Indicates that an entity is associated with or contains a school that exists only within a fictional or imaginary context.
-
E.
hasPublicLibraryBuilding
Indicates that an entity possesses or is associated with a public library building as a physical facility.
- 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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8b610a88190bc10fd7dda19da08 |
completed | March 7, 2026, 5:33 a.m. |
| PD | Predicate disambiguation | batch_69abb7a03a1c81909ad50d56667db2d5 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:37 p.m.