Triple
T12385512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen-Höchstadt |
E295851
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Spardorf
Spardorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, situated near the city of Erlangen.
|
E1035994
|
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: Spardorf | Statement: [Erlangen-Höchstadt, containsMunicipality, Spardorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spardorf Context triple: [Erlangen-Höchstadt, containsMunicipality, Spardorf]
-
A.
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
-
B.
Sierksdorf
Sierksdorf is a small coastal municipality in northern Germany, known for its Baltic Sea beaches and the Hansa-Park amusement park.
-
C.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
-
D.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
-
E.
Langdorf
Langdorf is a small municipality in the Bavarian Forest region of southeastern Germany.
- 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: Spardorf Triple: [Erlangen-Höchstadt, containsMunicipality, Spardorf]
Generated description
Spardorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, situated near the city of Erlangen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Spardorf Target entity description: Spardorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, situated near the city of Erlangen.
-
A.
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
-
B.
Sierksdorf
Sierksdorf is a small coastal municipality in northern Germany, known for its Baltic Sea beaches and the Hansa-Park amusement park.
-
C.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
-
D.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
-
E.
Langdorf
Langdorf is a small municipality in the Bavarian Forest region of southeastern Germany.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbd489c819098233a111442762e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f726570a2481909f417be6e38d283a |
completed | May 3, 2026, 10:41 a.m. |
| NEDg | Description generation | batch_69f726e03c4081908a0a07729eb30906 |
completed | May 3, 2026, 10:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f727d063c4819084b4990a0d759f79 |
completed | May 3, 2026, 10:47 a.m. |
Created at: April 8, 2026, 9:54 p.m.