Triple
T9197527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hilda Geiringer |
E220753
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Geiringer
Geiringer is a surname most notably associated with Hilda Geiringer, an Austrian-American mathematician known for her contributions to applied mathematics and probability theory.
|
E784071
|
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: Geiringer | Statement: [Hilda Geiringer, familyName, Geiringer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geiringer Context triple: [Hilda Geiringer, familyName, Geiringer]
-
A.
Khinchin
Khinchin is a Russian surname most notably associated with Aleksandr Khinchin, a prominent mathematician known for his contributions to probability theory and number theory.
-
B.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
C.
Bickel
Bickel is the family name of the American actor Fredric March, a prominent star of classic Hollywood cinema.
-
D.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
-
E.
Paepcke
Paepcke is a surname most notably associated with Walter Paepcke, the American industrialist and cultural philanthropist who founded the Aspen Institute.
- 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: Geiringer Triple: [Hilda Geiringer, familyName, Geiringer]
Generated description
Geiringer is a surname most notably associated with Hilda Geiringer, an Austrian-American mathematician known for her contributions to applied mathematics and probability theory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Geiringer Target entity description: Geiringer is a surname most notably associated with Hilda Geiringer, an Austrian-American mathematician known for her contributions to applied mathematics and probability theory.
-
A.
Khinchin
Khinchin is a Russian surname most notably associated with Aleksandr Khinchin, a prominent mathematician known for his contributions to probability theory and number theory.
-
B.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
C.
Bickel
Bickel is the family name of the American actor Fredric March, a prominent star of classic Hollywood cinema.
-
D.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
-
E.
Paepcke
Paepcke is a surname most notably associated with Walter Paepcke, the American industrialist and cultural philanthropist who founded the Aspen Institute.
- 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd87f50e88190940e73af1deda747 |
completed | April 1, 2026, 8:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c3b1af48190bb03af15232c510d |
completed | April 4, 2026, 12:32 a.m. |
| NEDg | Description generation | batch_69d05cfb7058819080d80b7f28125199 |
completed | April 4, 2026, 12:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d05d91ad4c8190a45db7484b9f058a |
completed | April 4, 2026, 12:38 a.m. |
Created at: March 30, 2026, 7:25 p.m.