Triple
T19542156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liza Huber |
E488932
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Huber |
—
|
NE NERFINISHED |
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: Huber | Statement: [Liza Huber, familyName, Huber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huber Context triple: [Liza Huber, familyName, Huber]
-
A.
Huber
chosen
Huber is a surname of German origin that is borne by various notable individuals across fields such as science, sports, and the arts.
-
B.
M. Huber
M. Huber is an abbreviated form of the personal name Michael Huber, commonly used in written references or citations.
-
C.
Schwarzhuber
Schwarzhuber is a German surname most notably associated with Johann Schwarzhuber, an SS officer and concentration camp official during World War II.
-
D.
Hilberseimer
Hilberseimer is the surname of Ludwig Hilberseimer, a German-American architect and urban planner known for his influential modernist city planning theories.
-
E.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6387442588190a2cdae60a6a940d6 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 10, 2026, 1:41 p.m.