Triple
T1016151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Court of Nuremberg |
E21934
|
entity |
| Predicate | belongsToCourtSystem |
P8214
|
FINISHED |
| Object | German ordinary courts |
—
|
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 ordinary courts | Statement: [Local Court of Nuremberg, belongsToCourtSystem, German ordinary courts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToCourtSystem Context triple: [Local Court of Nuremberg, belongsToCourtSystem, German ordinary courts]
-
A.
associatedWithCourt
Indicates a relationship in which an entity is linked or connected to a specific court, such as through jurisdiction, affiliation, or involvement in legal proceedings.
-
B.
hasTypeOfCourt
chosen
Indicates that an entity is associated with or classified by a specific type or category of court.
-
C.
affectedCourt
Indicates that a particular court is impacted or influenced by a specified action, decision, or legal matter.
-
D.
hasCourts
Indicates that an entity possesses, contains, or is equipped with one or more courts (e.g., legal, sports, or judicial facilities).
-
E.
associatedCourtCase
Indicates a relationship where one entity is linked to, or involved in, a particular court case.
- 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_69a493c68e24819080ed0ee8bcfd5ce0 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7c1e9d08190baf7e81f3777168d |
completed | March 1, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69a4b7238d4c8190b22d6c2ac0ac4911 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.