Triple
T12595067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin |
E300710
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Gronau |
E473976
|
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: Gronau | Statement: [Martin, twinTown, Gronau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gronau Context triple: [Martin, twinTown, Gronau]
-
A.
Gronau
chosen
Gronau is a town in Germany historically noted as the site of a battle during the Seven Years' War.
-
B.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
-
C.
Grüneberg
Grüneberg is a locality in Germany historically known as the site of the Battle of Grüneberg.
-
D.
Greußen
Greußen is a small town in the Kyffhäuserkreis district of Thuringia in central Germany, known for its rural character and regional historical heritage.
-
E.
Osterburg
Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954cde3c0819094e74413d6dcf548 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8c0510081909459662cb91a82b4 |
completed | May 3, 2026, 2:53 a.m. |
Created at: April 9, 2026, 5:08 p.m.