Triple
T715515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hesse |
E14304
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Fulda
Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
|
E161070
|
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: Fulda | Statement: [Hesse, containsCity, Fulda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fulda Context triple: [Hesse, containsCity, Fulda]
-
A.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
B.
Würzburg
Würzburg is a historic city in southern Germany known for its baroque architecture, the Würzburg Residence palace, and its location along the Main River in the Franconia wine region.
-
C.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
D.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
E.
Weilburg
Weilburg is a historic town in the German state of Hesse, known for its Renaissance castle and as the ancestral seat of the House of Nassau-Weilburg.
- 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: Fulda Triple: [Hesse, containsCity, Fulda]
Generated description
Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fulda Target entity description: Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
-
A.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
B.
Würzburg
Würzburg is a historic city in southern Germany known for its baroque architecture, the Würzburg Residence palace, and its location along the Main River in the Franconia wine region.
-
C.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
D.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
E.
Weilburg
Weilburg is a historic town in the German state of Hesse, known for its Renaissance castle and as the ancestral seat of the House of Nassau-Weilburg.
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a574b4d881908b6d0be386081efd |
completed | March 1, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace5371c2c8190861a5cbf9d089e4f |
completed | March 8, 2026, 2:55 a.m. |
| NEDg | Description generation | batch_69ace5db553c8190b0d09462411f3dcf |
completed | March 8, 2026, 2:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ace647c04881908ab550505110c29b |
completed | March 8, 2026, 3 a.m. |
Created at: March 1, 2026, 7:37 p.m.