Triple
T921390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Lippe |
E19891
|
entity |
| Predicate | associatedTerritory |
P1103
|
FINISHED |
| Object | Lippe |
E138916
|
NE 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: Lippe | Statement: [House of Lippe, associatedTerritory, Lippe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lippe Context triple: [House of Lippe, associatedTerritory, Lippe]
-
A.
Lippe
chosen
Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
-
B.
Leine
The Leine is a major river in central Germany that flows through the federal state of Lower Saxony, passing cities such as Göttingen and Hanover before joining the Aller.
-
C.
Weser
The Weser is a major river in northwestern Germany that flows through several federal states before emptying into the North Sea.
-
D.
Regnitz
The Regnitz is a river in the German state of Bavaria that flows through cities such as Erlangen and Bamberg before joining the Main River.
-
E.
Werra
The Werra is a major river in central Germany that forms one of the two headstreams of the Weser.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b313cb908190ad78b3a54e4f2eb7 |
completed | March 1, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac99669a1481909fa42162ffee2d7b |
completed | March 7, 2026, 9:32 p.m. |
Created at: March 1, 2026, 7:40 p.m.