Triple
T2513431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorenz Hackenholt |
E52752
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Hackenholt
Hackenholt is a German surname most notably associated with Lorenz Hackenholt, an SS officer involved in the Nazi extermination camps during World War II.
|
E285608
|
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: Hackenholt | Statement: [Lorenz Hackenholt, familyName, Hackenholt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hackenholt Context triple: [Lorenz Hackenholt, familyName, Hackenholt]
-
A.
Haaksbergen
Haaksbergen is a town in the eastern Netherlands, near the German border, known for its rural surroundings and cross-border ties with neighboring German communities.
-
B.
Holendrecht
Holendrecht is a metro station in Amsterdam serving the southeastern part of the city, including the nearby academic hospital and university campus.
-
C.
Ridderkerk
Ridderkerk is a town and municipality in the western Netherlands, situated near Rotterdam in the province of South Holland.
-
D.
Hulst
Hulst is a historic fortified town and municipality in the Dutch province of Zeeland, near the border with Belgium.
-
E.
Soestdijk
Soestdijk is a village in the Netherlands known for its historic royal residence, Soestdijk Palace.
- 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: Hackenholt Triple: [Lorenz Hackenholt, familyName, Hackenholt]
Generated description
Hackenholt is a German surname most notably associated with Lorenz Hackenholt, an SS officer involved in the Nazi extermination camps during World War II.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hackenholt Target entity description: Hackenholt is a German surname most notably associated with Lorenz Hackenholt, an SS officer involved in the Nazi extermination camps during World War II.
-
A.
Haaksbergen
Haaksbergen is a town in the eastern Netherlands, near the German border, known for its rural surroundings and cross-border ties with neighboring German communities.
-
B.
Holendrecht
Holendrecht is a metro station in Amsterdam serving the southeastern part of the city, including the nearby academic hospital and university campus.
-
C.
Ridderkerk
Ridderkerk is a town and municipality in the western Netherlands, situated near Rotterdam in the province of South Holland.
-
D.
Hulst
Hulst is a historic fortified town and municipality in the Dutch province of Zeeland, near the border with Belgium.
-
E.
Soestdijk
Soestdijk is a village in the Netherlands known for its historic royal residence, Soestdijk Palace.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd20b6d008190acec0eb172e218c9 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98a8887c8190bd00eaf48bc77781 |
completed | March 10, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69af99e3e4bc819080ac8a379592c6d2 |
completed | March 10, 2026, 4:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af9a3716e08190a559684dbc7df774 |
completed | March 10, 2026, 4:12 a.m. |
Created at: March 6, 2026, 9:46 p.m.