Triple
T11999094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorenz Hackenholt |
E285608
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hackenholt |
E285608
|
NE FINISHED |
How this triple was built (2 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.
Hackenholt
chosen
Hackenholt is a German surname most notably associated with Lorenz Hackenholt, an SS officer involved in the Nazi extermination camps during World War II.
-
B.
Holleken
Holleken is a local railway station serving the suburban area near Brussels, Belgium.
-
C.
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.
-
D.
Land van Heusden
Land van Heusden is a historic region in the northern part of the Dutch province of North Brabant, centered around the town of Heusden and known for its medieval fortifications and river landscapes.
-
E.
Bezuidenhout
Bezuidenhout is a neighborhood in The Hague, Netherlands, known for its residential character and proximity to major government and business districts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c26d7881909b67a31d04882eb5 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49d183ee881908f1c0ca4562344a9 |
completed | May 1, 2026, 12:31 p.m. |
Created at: April 8, 2026, 9:46 p.m.