Triple
T12877801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig metropolitan region |
E308012
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Querfurt
Querfurt is a small historic town in the German state of Saxony-Anhalt, known for its well-preserved medieval castle and old town.
|
E1131283
|
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: Querfurt | Statement: [Leipzig metropolitan region, containsCity, Querfurt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Querfurt Context triple: [Leipzig metropolitan region, containsCity, Querfurt]
-
A.
Nordhausen
Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
-
B.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
C.
Helmstedt
Helmstedt is a historic town in Lower Saxony, Germany, known for its medieval architecture and former university.
-
D.
Rudolstadt
Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
-
E.
Zerbst
Zerbst is a historic town in Saxony-Anhalt, Germany, known as the birthplace of Catherine the Great and for its former role as a princely residence.
- 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: Querfurt Triple: [Leipzig metropolitan region, containsCity, Querfurt]
Generated description
Querfurt is a small historic town in the German state of Saxony-Anhalt, known for its well-preserved medieval castle and old town.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Querfurt Target entity description: Querfurt is a small historic town in the German state of Saxony-Anhalt, known for its well-preserved medieval castle and old town.
-
A.
Nordhausen
Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
-
B.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
C.
Helmstedt
Helmstedt is a historic town in Lower Saxony, Germany, known for its medieval architecture and former university.
-
D.
Rudolstadt
Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
-
E.
Zerbst
Zerbst is a historic town in Saxony-Anhalt, Germany, known as the birthplace of Catherine the Great and for its former role as a princely residence.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe967a96708190b8688e84fdb270b0 |
completed | May 9, 2026, 2:05 a.m. |
| NEDg | Description generation | batch_69fe97c0a3688190a9f55376a1d7ad87 |
completed | May 9, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe985cec608190888733cdc5bd71ea |
completed | May 9, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:38 p.m.