Triple
T10988135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haßberge (district) |
E259683
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kleinmünster
Kleinmünster is a small locality in the Haßberge district of northern Bavaria, Germany.
|
E898331
|
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: Kleinmünster | Statement: [Haßberge (district), contains, Kleinmünster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kleinmünster Context triple: [Haßberge (district), contains, Kleinmünster]
-
A.
Heilig-Kreuz-Münster
Heilig-Kreuz-Münster is a prominent historic Catholic church and architectural landmark in the town of Schwäbisch Gmünd in southern Germany.
-
B.
Bergkirche
Bergkirche is a historic Baroque pilgrimage church in Eisenstadt, Austria, best known for its association with composer Joseph Haydn and its distinctive hilltop setting.
-
C.
Marienmünster Dießen
Marienmünster Dießen is a historic former monastery church and prominent Catholic pilgrimage site in the Bavarian town of Dießen am Ammersee.
-
D.
Tannenkirch
Tannenkirch is a village in the Black Forest region of southwestern Germany that forms one of the districts of the town of Kandern in Baden-Württemberg.
-
E.
Altenmünster
Altenmünster is a municipality in the Swabian region of Bavaria, Germany, known for its rural character and location within the Augsburg district.
- 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: Kleinmünster Triple: [Haßberge (district), contains, Kleinmünster]
Generated description
Kleinmünster is a small locality in the Haßberge district of northern Bavaria, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kleinmünster Target entity description: Kleinmünster is a small locality in the Haßberge district of northern Bavaria, Germany.
-
A.
Heilig-Kreuz-Münster
Heilig-Kreuz-Münster is a prominent historic Catholic church and architectural landmark in the town of Schwäbisch Gmünd in southern Germany.
-
B.
Bergkirche
Bergkirche is a historic Baroque pilgrimage church in Eisenstadt, Austria, best known for its association with composer Joseph Haydn and its distinctive hilltop setting.
-
C.
Marienmünster Dießen
Marienmünster Dießen is a historic former monastery church and prominent Catholic pilgrimage site in the Bavarian town of Dießen am Ammersee.
-
D.
Tannenkirch
Tannenkirch is a village in the Black Forest region of southwestern Germany that forms one of the districts of the town of Kandern in Baden-Württemberg.
-
E.
Altenmünster
Altenmünster is a municipality in the Swabian region of Bavaria, Germany, known for its rural character and location within the Augsburg district.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b574d08190adec34b814a26437 |
completed | April 9, 2026, 11:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e344f95ab88190bbce8f0eab0b2713 |
completed | April 18, 2026, 8:46 a.m. |
| NEDg | Description generation | batch_69e3556e8b408190a02a1fe194ae5750 |
completed | April 18, 2026, 9:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3591ecd548190b049ce95fe3f86d9 |
completed | April 18, 2026, 10:12 a.m. |
Created at: April 8, 2026, 9:24 p.m.