Triple
T10711675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Höxter |
E252551
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Daniel Hartmann
Daniel Hartmann is a German local politician who serves as the mayor of the town of Höxter in North Rhine-Westphalia.
|
E893104
|
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: Daniel Hartmann | Statement: [Höxter, hasMayor, Daniel Hartmann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Hartmann Context triple: [Höxter, hasMayor, Daniel Hartmann]
-
A.
Robert Hartmann
Robert Hartmann is a technology entrepreneur best known as a founder of the semiconductor company Altera.
-
B.
Richard Hönigswald
Richard Hönigswald was a German Neo-Kantian philosopher known for his work on epistemology, logic, and the philosophy of science in the early 20th century.
-
C.
Walter Hartmann
Walter Hartmann was a German Wehrmacht general who held senior command positions, including corps-level leadership, during World War II.
-
D.
Marten Wassmann
Marten Wassmann is an architect known for his partnership role at the Dutch architecture firm Benthem Crouwel Architekten.
-
E.
David Ortkiese
David Ortkiese is a cinematographer known for his work on the film "A Haunted House."
- 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: Daniel Hartmann Triple: [Höxter, hasMayor, Daniel Hartmann]
Generated description
Daniel Hartmann is a German local politician who serves as the mayor of the town of Höxter in North Rhine-Westphalia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Hartmann Target entity description: Daniel Hartmann is a German local politician who serves as the mayor of the town of Höxter in North Rhine-Westphalia.
-
A.
Robert Hartmann
Robert Hartmann is a technology entrepreneur best known as a founder of the semiconductor company Altera.
-
B.
Richard Hönigswald
Richard Hönigswald was a German Neo-Kantian philosopher known for his work on epistemology, logic, and the philosophy of science in the early 20th century.
-
C.
Walter Hartmann
Walter Hartmann was a German Wehrmacht general who held senior command positions, including corps-level leadership, during World War II.
-
D.
Marten Wassmann
Marten Wassmann is an architect known for his partnership role at the Dutch architecture firm Benthem Crouwel Architekten.
-
E.
David Ortkiese
David Ortkiese is a cinematographer known for his work on the film "A Haunted House."
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fe523de08190a82c8f057fe8baf6 |
completed | April 9, 2026, 1:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2161018408190bcb64efba0974f8c |
completed | April 17, 2026, 11:14 a.m. |
| NEDg | Description generation | batch_69e21d860d288190855ffbe60df50df9 |
completed | April 17, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e21f09be508190a7c497a7680cb59e |
completed | April 17, 2026, 11:52 a.m. |
Created at: April 8, 2026, 9:13 p.m.