Triple
T8778282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pforzheim |
E208654
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Peter Boch
Peter Boch is a German politician who serves as the mayor of the city of Pforzheim in Baden-Württemberg.
|
E757060
|
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: Peter Boch | Statement: [Pforzheim, hasMayor, Peter Boch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Boch Context triple: [Pforzheim, hasMayor, Peter Boch]
-
A.
Walter Scheib
Walter Scheib was an American chef best known for serving as the White House Executive Chef for the Clinton and George W. Bush administrations.
-
B.
Thomas Borsch
Thomas Borsch is a German botanist and academic known for his leadership of the Berlin Botanical Garden and his research on plant systematics and biodiversity.
-
C.
Walter Frentz
Walter Frentz was a German cameraman and filmmaker known for his close collaboration with Leni Riefenstahl and his work on Nazi propaganda films.
-
D.
Carlos Holzwarth
Carlos Holzwarth was an art instructor and mentor known for teaching and influencing painter Armin Hansen.
-
E.
Percy Becker
Percy Becker is a supporting character in the 2023 romantic comedy film "No Hard Feelings," involved in the story surrounding the socially awkward teenager Percy and his unconventional coming-of-age experiences.
- 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: Peter Boch Triple: [Pforzheim, hasMayor, Peter Boch]
Generated description
Peter Boch is a German politician who serves as the mayor of the city of Pforzheim in Baden-Württemberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Boch Target entity description: Peter Boch is a German politician who serves as the mayor of the city of Pforzheim in Baden-Württemberg.
-
A.
Walter Scheib
Walter Scheib was an American chef best known for serving as the White House Executive Chef for the Clinton and George W. Bush administrations.
-
B.
Thomas Borsch
Thomas Borsch is a German botanist and academic known for his leadership of the Berlin Botanical Garden and his research on plant systematics and biodiversity.
-
C.
Walter Frentz
Walter Frentz was a German cameraman and filmmaker known for his close collaboration with Leni Riefenstahl and his work on Nazi propaganda films.
-
D.
Carlos Holzwarth
Carlos Holzwarth was an art instructor and mentor known for teaching and influencing painter Armin Hansen.
-
E.
Percy Becker
Percy Becker is a supporting character in the 2023 romantic comedy film "No Hard Feelings," involved in the story surrounding the socially awkward teenager Percy and his unconventional coming-of-age experiences.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f51b3d48190b542a0423d3938e0 |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf51d69af481909245ca327f36e9c2 |
completed | April 3, 2026, 5:36 a.m. |
| NEDg | Description generation | batch_69cf545edf648190bb7d79a75cd9ba99 |
completed | April 3, 2026, 5:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf5513f894819087a2c39142d597b4 |
completed | April 3, 2026, 5:50 a.m. |
Created at: March 30, 2026, 6:42 p.m.