Triple
T9214370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konrad Meyer-Hetling |
E221205
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Meyer-Hetling
Meyer-Hetling is a German surname most notably associated with Konrad Meyer-Hetling, an agronomist and SS officer involved in Nazi settlement planning.
|
E785522
|
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: Meyer-Hetling | Statement: [Konrad Meyer-Hetling, familyName, Meyer-Hetling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meyer-Hetling Context triple: [Konrad Meyer-Hetling, familyName, Meyer-Hetling]
-
A.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
B.
Löwenthal
Löwenthal is the maiden surname of Elsa Einstein, who was both the second wife and cousin of physicist Albert Einstein.
-
C.
Weinert
Weinert is a German-language surname borne by various notable individuals in fields such as the arts, sciences, and public life.
-
D.
Heurich
Heurich is a German surname most notably associated with Christian Heurich, a prominent brewer and businessman in Washington, D.C.
-
E.
Oberhauser
Oberhauser is a German-language surname borne by various notable individuals across fields such as sports, the arts, and public life.
- 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: Meyer-Hetling Triple: [Konrad Meyer-Hetling, familyName, Meyer-Hetling]
Generated description
Meyer-Hetling is a German surname most notably associated with Konrad Meyer-Hetling, an agronomist and SS officer involved in Nazi settlement planning.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meyer-Hetling Target entity description: Meyer-Hetling is a German surname most notably associated with Konrad Meyer-Hetling, an agronomist and SS officer involved in Nazi settlement planning.
-
A.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
B.
Löwenthal
Löwenthal is the maiden surname of Elsa Einstein, who was both the second wife and cousin of physicist Albert Einstein.
-
C.
Weinert
Weinert is a German-language surname borne by various notable individuals in fields such as the arts, sciences, and public life.
-
D.
Heurich
Heurich is a German surname most notably associated with Christian Heurich, a prominent brewer and businessman in Washington, D.C.
-
E.
Oberhauser
Oberhauser is a German-language surname borne by various notable individuals across fields such as sports, the arts, and public life.
- 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_69ca83eae42c8190a0ea9e040710a277 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda06bf80819094c6e74b4b6a31e4 |
completed | April 1, 2026, 8:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d06613daf88190a0128fd53ea1b134 |
completed | April 4, 2026, 1:15 a.m. |
| NEDg | Description generation | batch_69d0678b89ac8190b807e1c3b457a503 |
completed | April 4, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0688d4c388190bb024b03cc86d08f |
completed | April 4, 2026, 1:25 a.m. |
Created at: March 30, 2026, 7:27 p.m.