Triple
T14677681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marianne Huber |
E344690
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Huber |
E79860
|
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: Huber | Statement: [Marianne Huber, hasFamilyName, Huber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huber Context triple: [Marianne Huber, hasFamilyName, 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.
Schwarzhuber
Schwarzhuber is a German surname most notably associated with Johann Schwarzhuber, an SS officer and concentration camp official during World War II.
-
C.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
D.
Eberl
Eberl is a German-language surname of Austrian and Bavarian origin borne by various notable individuals.
-
E.
Neubauer
Neubauer is a German surname borne by various notable individuals, including activists, politicians, and academics.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb567c2b88190a9639e61b6fba7df |
completed | April 14, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde17e5a1c8190b1bf5565eab9d519 |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:27 a.m.