Triple
T5371361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Hesse |
E108856
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Fritzlar
Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
|
E630207
|
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: Fritzlar | Statement: [North Hesse, hasCity, Fritzlar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fritzlar Context triple: [North Hesse, hasCity, Fritzlar]
-
A.
Bruchsal
Bruchsal is a town in the state of Baden-Württemberg in southwestern Germany, known for its baroque palace and asparagus cultivation.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
C.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
D.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
E.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
- 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: Fritzlar Triple: [North Hesse, hasCity, Fritzlar]
Generated description
Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fritzlar Target entity description: Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
-
A.
Bruchsal
Bruchsal is a town in the state of Baden-Württemberg in southwestern Germany, known for its baroque palace and asparagus cultivation.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
C.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
D.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
E.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
- 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_69c75800b55081909d6073f10ff16f08 |
completed | March 28, 2026, 4:24 a.m. |
| NEDg | Description generation | batch_69c758cc67d88190a8f9325b757f9baa |
completed | March 28, 2026, 4:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c75943b34481909c4fb40cc22bba3b |
completed | March 28, 2026, 4:29 a.m. |
Created at: March 20, 2026, 2:02 p.m.