Triple
T21275523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pont-à-Mousson |
E524376
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object |
Hemmingen
Hemmingen is a municipality in Germany, known as a small town that engages in international town twinning partnerships.
|
E1475225
|
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: Hemmingen | Statement: [Pont-à-Mousson, twinnedWith, Hemmingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hemmingen Context triple: [Pont-à-Mousson, twinnedWith, Hemmingen]
-
A.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
B.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
C.
Hochemmerich
Hochemmerich is a district of the Rheinhausen area in Duisburg, Germany, known for its residential neighborhoods and proximity to the Rhine River.
-
D.
Hedingen
Hedingen is a small municipality in the canton of Zurich in Switzerland, situated in the district of Affoltern within the Albis region.
-
E.
Meinerzhagen
Meinerzhagen is a town in western Germany known for its location in the hilly, forested Sauerland region of North Rhine-Westphalia.
- 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: Hemmingen Triple: [Pont-à-Mousson, twinnedWith, Hemmingen]
Generated description
Hemmingen is a municipality in Germany, known as a small town that engages in international town twinning partnerships.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hemmingen Target entity description: Hemmingen is a municipality in Germany, known as a small town that engages in international town twinning partnerships.
-
A.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
B.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
C.
Hochemmerich
Hochemmerich is a district of the Rheinhausen area in Duisburg, Germany, known for its residential neighborhoods and proximity to the Rhine River.
-
D.
Hedingen
Hedingen is a small municipality in the canton of Zurich in Switzerland, situated in the district of Affoltern within the Albis region.
-
E.
Meinerzhagen
Meinerzhagen is a town in western Germany known for its location in the hilly, forested Sauerland region of North Rhine-Westphalia.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7365627a081908caea09097cca354 |
completed | April 21, 2026, 8:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a099008c7788190a9b49d876eb02161 |
completed | May 17, 2026, 9:53 a.m. |
| NEDg | Description generation | batch_6a0991a69afc8190bf48f6485998d442 |
completed | May 17, 2026, 10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09922cc57c8190bbf6ce0916e43571 |
completed | May 17, 2026, 10:02 a.m. |
Created at: April 16, 2026, 4:02 p.m.