Triple
T5371357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Hesse |
E108856
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Hofgeismar
Hofgeismar is a small historic town in the German state of Hesse, known for its medieval architecture and picturesque setting.
|
E518156
|
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: Hofgeismar | Statement: [North Hesse, hasCity, Hofgeismar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hofgeismar Context triple: [North Hesse, hasCity, Hofgeismar]
-
A.
Bückeburg
Bückeburg is a historic town in Lower Saxony, Germany, known for its former role as the residence of the Counts and Princes of Schaumburg-Lippe and its well-preserved Renaissance castle.
-
B.
Höxter
Höxter is a historic town in eastern North Rhine-Westphalia, Germany, known for its location on the River Weser and proximity to the UNESCO-listed Corvey Abbey.
-
C.
Northeim
Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
-
D.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
E.
Neu-Isenburg
Neu-Isenburg is a town in the Offenbach district of Hesse, Germany, located near Frankfurt am Main and known for its residential character and proximity to major transport routes.
- 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: Hofgeismar Triple: [North Hesse, hasCity, Hofgeismar]
Generated description
Hofgeismar is a small historic town in the German state of Hesse, known for its medieval architecture and picturesque setting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hofgeismar Target entity description: Hofgeismar is a small historic town in the German state of Hesse, known for its medieval architecture and picturesque setting.
-
A.
Bückeburg
Bückeburg is a historic town in Lower Saxony, Germany, known for its former role as the residence of the Counts and Princes of Schaumburg-Lippe and its well-preserved Renaissance castle.
-
B.
Höxter
Höxter is a historic town in eastern North Rhine-Westphalia, Germany, known for its location on the River Weser and proximity to the UNESCO-listed Corvey Abbey.
-
C.
Northeim
Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
-
D.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
E.
Neu-Isenburg
Neu-Isenburg is a town in the Offenbach district of Hesse, Germany, located near Frankfurt am Main and known for its residential character and proximity to major transport routes.
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd86aa0f5c8190ba96554e75696f8e |
completed | March 20, 2026, 5:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf3a94acc48190a4c0ea39c6b8a405 |
completed | March 22, 2026, 12:40 a.m. |
| NEDg | Description generation | batch_69bf3b2a1a888190b4c4191ec6b98b42 |
completed | March 22, 2026, 12:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf3b8170748190b2e7e427323d55f9 |
completed | March 22, 2026, 12:44 a.m. |
Created at: March 20, 2026, 2:02 p.m.