Triple
T2983107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont-Saint-Aignan |
E80556
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Barsinghausen
Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
|
E334711
|
NE FINISHED |
How this triple was built (4 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: Barsinghausen | Statement: [Mont-Saint-Aignan, hasTwinTown, Barsinghausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barsinghausen Context triple: [Mont-Saint-Aignan, hasTwinTown, Barsinghausen]
-
A.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
B.
Korbach
Korbach is a historic town in the German state of Hesse, known as the district seat of Waldeck-Frankenberg and for its well-preserved medieval old town.
-
C.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
-
D.
Wallhausen
Wallhausen is a village in present-day Saxony-Anhalt, Germany, historically notable as the birthplace of Otto I, Holy Roman Emperor.
-
E.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Barsinghausen Triple: [Mont-Saint-Aignan, hasTwinTown, Barsinghausen]
Generated description
Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barsinghausen Target entity description: Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
-
A.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
B.
Korbach
Korbach is a historic town in the German state of Hesse, known as the district seat of Waldeck-Frankenberg and for its well-preserved medieval old town.
-
C.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
-
D.
Wallhausen
Wallhausen is a village in present-day Saxony-Anhalt, Germany, historically notable as the birthplace of Otto I, Holy Roman Emperor.
-
E.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
- F. None of above. chosen
Provenance (5 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99a1ed44819085ae6d39943db1d9 |
completed | March 8, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b23592a4888190a78fcae60f4971dd |
completed | March 12, 2026, 3:40 a.m. |
| NEDg | Description generation | batch_69b2395546948190bb9b972138324f2e |
completed | March 12, 2026, 3:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b23d1fd35c819096f2dd6f26ea697d |
completed | March 12, 2026, 4:12 a.m. |
Created at: March 8, 2026, 2:58 p.m.