Triple
T1002878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanover |
E21642
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Rouen |
E51605
|
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: Rouen | Statement: [Hanover, twinCity, Rouen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rouen Context triple: [Hanover, twinCity, Rouen]
-
A.
Rouen
chosen
Rouen is a historic city in northern France renowned for its medieval architecture, Gothic cathedral, and association with figures like Joan of Arc and the Impressionist painter Claude Monet.
-
B.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
C.
Amiens
Amiens is a historic city in northern France, known for its Gothic cathedral and role as the site of the 1802 Treaty of Amiens.
-
D.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
-
E.
Cherbourg
Cherbourg is a major French port city on the Cotentin Peninsula, known for its strategic naval harbor and cross-Channel ferry connections.
- 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fe0a548190aee8abf1890e141e |
completed | March 1, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad589d25308190afa71150fdbaed7a |
completed | March 8, 2026, 11:08 a.m. |
Created at: March 1, 2026, 7:41 p.m.