Triple
T13025221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ahrensfelde |
E326286
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Hoppegarten |
E895733
|
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: Hoppegarten | Statement: [Ahrensfelde, hasBorderWith, Hoppegarten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hoppegarten Context triple: [Ahrensfelde, hasBorderWith, Hoppegarten]
-
A.
Hoppegarten
chosen
Hoppegarten is a municipality in the Märkisch-Oderland district of Brandenburg, Germany, best known for its historic horse racing track just east of Berlin.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Berggarten
Berggarten is a historic botanical garden in Hanover, Germany, renowned for its diverse plant collections and greenhouses.
-
D.
Marienhof
Marienhof is a German television soap opera that gained popularity in the 1990s and 2000s for its portrayal of everyday life and relationships in a fictional Cologne neighborhood.
-
E.
Leingarten
Leingarten is a municipality in the Heilbronn district of Baden-Württemberg, Germany, known for its wine-growing tradition and location near the city of Heilbronn.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efac71881908a21d70c3c6ce099 |
completed | April 10, 2026, 10:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c11c1f6c8190be1c570a7e44a313 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:53 p.m.