Triple
T13110610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hof (Saale) |
E310959
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Hof-Innenstadt
Hof-Innenstadt is the central urban district and main commercial area of the city of Hof in Bavaria, Germany.
|
E1023074
|
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: Hof-Innenstadt | Statement: [Hof (Saale), hasSubdivision, Hof-Innenstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hof-Innenstadt Context triple: [Hof (Saale), hasSubdivision, Hof-Innenstadt]
-
A.
Maxvorstadt
Maxvorstadt is a central Munich district known for its concentration of major art museums, universities, and cultural institutions.
-
B.
Innere Neustadt
Innere Neustadt is a historic central district of Dresden, Germany, known for its Baroque architecture, cultural venues, and vibrant urban life along the Elbe River.
-
C.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
D.
Innere Altstadt
Innere Altstadt is the historic core of Dresden, Germany, known for its baroque architecture, cultural landmarks, and concentration of major city attractions.
-
E.
Rathausviertel
Rathausviertel is a central district in Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to landmarks such as the city hall and major parks.
- 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: Hof-Innenstadt Triple: [Hof (Saale), hasSubdivision, Hof-Innenstadt]
Generated description
Hof-Innenstadt is the central urban district and main commercial area of the city of Hof in Bavaria, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hof-Innenstadt Target entity description: Hof-Innenstadt is the central urban district and main commercial area of the city of Hof in Bavaria, Germany.
-
A.
Maxvorstadt
Maxvorstadt is a central Munich district known for its concentration of major art museums, universities, and cultural institutions.
-
B.
Innere Neustadt
Innere Neustadt is a historic central district of Dresden, Germany, known for its Baroque architecture, cultural venues, and vibrant urban life along the Elbe River.
-
C.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
D.
Innere Altstadt
Innere Altstadt is the historic core of Dresden, Germany, known for its baroque architecture, cultural landmarks, and concentration of major city attractions.
-
E.
Rathausviertel
Rathausviertel is a central district in Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to landmarks such as the city hall and major parks.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817e4f408190b77c198b4157d77a |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e27d8110819087ade3537f867ae0 |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e4c5e2888190b0bfcdf2cc25ad5f |
completed | May 3, 2026, 6:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e5979df881909db42a735b9b1064 |
completed | May 3, 2026, 6:05 a.m. |
Created at: April 9, 2026, 9:05 p.m.