Triple
T5582347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Margrethen |
E146668
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Höchst
Höchst is a municipality in the Austrian state of Vorarlberg, located near the Swiss border along the Rhine River.
|
E529388
|
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: Höchst | Statement: [St. Margrethen, hasNeighboringMunicipality, Höchst]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Höchst Context triple: [St. Margrethen, hasNeighboringMunicipality, Höchst]
-
A.
Höchst
Höchst is a historic district in western Frankfurt am Main, Germany, known for its well-preserved old town and former industrial and chemical industry sites.
-
B.
Oststadt
Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
-
C.
Grevesmühlen
Grevesmühlen is a small town in the German state of Mecklenburg-Vorpommern, known as a local administrative and service center in the north of the country.
-
D.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
-
E.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
- 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: Höchst Triple: [St. Margrethen, hasNeighboringMunicipality, Höchst]
Generated description
Höchst is a municipality in the Austrian state of Vorarlberg, located near the Swiss border along the Rhine River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Höchst Target entity description: Höchst is a municipality in the Austrian state of Vorarlberg, located near the Swiss border along the Rhine River.
-
A.
Höchst
Höchst is a historic district in western Frankfurt am Main, Germany, known for its well-preserved old town and former industrial and chemical industry sites.
-
B.
Oststadt
Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
-
C.
Grevesmühlen
Grevesmühlen is a small town in the German state of Mecklenburg-Vorpommern, known as a local administrative and service center in the north of the country.
-
D.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
-
E.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
- 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_69c0090287a08190b4098411effe970c |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0208333f08190bf0049b6bdd280f5 |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0285e7bc08190bd5a08c50679e9d9 |
completed | March 22, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69c037fca93881908d4d7403bfb1f866 |
completed | March 22, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03898327c8190bd3b889bd7663003 |
completed | March 22, 2026, 6:44 p.m. |
Created at: March 22, 2026, 3:37 p.m.