Triple
T6440710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allan Huber Selig |
E138214
|
entity |
| Predicate | middleName |
P143
|
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: [Allan Huber Selig, middleName, Huber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huber Context triple: [Allan Huber Selig, middleName, 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.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
C.
Eberl
Eberl is a German-language surname of Austrian and Bavarian origin borne by various notable individuals.
-
D.
Neubauer
Neubauer is a German surname borne by various notable individuals, including activists, politicians, and academics.
-
E.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
- 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_69c008aa61ac8190bc96715ed79fe2d8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06968c6a88190a623da9bd3152a66 |
completed | March 22, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bc1ccdc8190a171281a846173c0 |
completed | March 27, 2026, 9:20 a.m. |
Created at: March 22, 2026, 4:46 p.m.